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Accurate and reliable coding (or labeling) using humans and machines is central to both the qualitative research and the text analytics process. For new and experienced researchers, learning advanced project management skills adds hours of struggle. Since 2005, ( ) coders and project managers have delivered large-scale, high value-added, reliable and accurate annotated text data in a timely and expert manner. The QDAP approach to qualitative research grows out of more than a decade of working on projects funded by the National Science Foundation, the National Institutes of Health, DARPA, the Smithsonian and others. Our combination of software, methods, experience, and novel forms of text analytics measurement present a uniquely scientific research support offering with applications across the disciplines.

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Qualitative data analysis for text, images, audio, video. Cross platform. Python 3.10 or newer and PyQt6.

ccbogel/QualCoder

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QualCoder is a qualitative data analysis application written in Python.

Text files can be typed in manually or loaded from txt, odt, docx, html, htm, md, epub, and PDF files. Images, video, and audio can also be imported for coding. Codes can be assigned to text, images, and a/v selections and grouped into categories in a hierarchical fashion. Various types of reports can be produced including visual coding graphs, coder comparisons, and coding frequencies.

This software has been used on MacOS and various Linux distros. Instructions and other information are available here: https://qualcoder.wordpress.com/ and on the Github Wiki .

It is best to download the Current Release from the Releases page, see the Releases link in the right-hand column on this page.

If you like QualCoder please buy me a coffee ...

INSTALLATION

Prerequisites.

Optional: VLC for audio/video coding. Optional: ffmpeg installed for speech-to-text and waveform image see here to install ffmpeg on Windows: https://phoenixnap.com/kb/ffmpeg-windows .

For installing from source you will need to have Python 3.10 or a newer version installed.

Use the exe

Newer releases contain an exe file (created on Windows 10, 64-bit). Double-click to run. Look for the Releases link on the right-hand side of this page. I have had feedback on one instance on Windows where an anti-virus affected the importing and moving of files by QualCoder (AVG). An online virus testing site www.virustotal.com indicated 2 vendors out of many detected a potential problem due to their detection methods (false positives), 5 March 2022. Always check the MD5 checksum on downloading the exe. I have not got the exe Microsoft certified (I am not sure of the processes or cost involved). If you are uncomfortable with these warnings install from the source as detailed next.

Alternatively, install from source:

Seriously consider using a virtual environment (commands in point 6 below). Not using a virtual environment may affect other Python software you may have installed.

• Download and install the Python programming language. The minimum recommended version for QualCoder is 3.10. Python3 . Download the file (at the bottom of the website) "Windows installer (64-bit)"

IMPORTANT: in the first window of the installation mark the option "Add Python to PATH"

Download the QualCoder software from: https://github.com/ccbogel/QualCoder from the Green Code button. This is the newest, but not yet officially released code (occasionally coding errors creep in). Click the green button "Code", and then "Download ZIP". Alternatively , choose the most recent release zip, see the right-hand side of this page for the link to Releases.

Unzip the folder to a location (e.g. downloads). (Tip, remove the doubled-up folder extraction QualCoder-master\QualCoder-master when asked where to extract. Just QualCoder-master).

Use the Windows command prompt. Type "cmd" in the Windows Start search engine, and click on the black software "cmd.exe" - the command console for Windows. In the console type or paste, using the right-click mouse copy and paste (ctrl+v does not work)

In the command prompt, move (using the cd command) into the QualCoder folder. You should be inside the QualCoder-master folder or if using a release (the Qualcoder-3.5 folder). e.g.

• Install and activate the virtual environment. This step can be skipped, but I recommend you do not skip it.

The py command uses the most recent installed version of Python the py command does not work on all Windows OS, you may instead replace py with python3 You can use a specific version on your Windows if you have many Python versions installed, e.g. py -3.10 See discussion here: Difference between py and python

We recommend using a virtual environment to install packages. This will ensure that the dependencies for QualCoder are isolated from the rest of your system. On some Windows OS you may need to replace the py command with python3 below:

• Install python modules. Type the following to upgrade all python modules before importing:

Type the following to install the required modules:

Wait, until all modules are installed.

Note: on some Windows computers, you may have to type python3 instead of py as py may not be recognised.

• Run QualCoder from the command prompt
• If running QualCoder in a virtual environment, to exit the virtual environment type:

The command prompt will then remove the (env) wording.

To start QualCoder again

If you are not using a virtual environment, as long as you are in the same drive letter, eg C:

py -m qualcoder

If you are using a virtual environment:

cd to the Qualcoder-master (or Qualcoder release folder), then type:

env\Scripts\activate.bat

Debian/Ubuntu Linux

There is an executable file (double-click to run) for Ubuntu 22 in the 3.5 release. Alternatively, install from source code below. It is best to run QualCoder inside a Python virtual environment so that the system-installed python modules do not clash and cause problems. If you are using the alternative Ubuntu Desktop manager Xfce you may need to run this: sudo apt install libxcb-cursor0

• Recommend that you install vlc (download from site) or:

sudo apt install vlc

• Install pip

sudo apt install python3-pip

• Install venv I am using python3.10 you can choose another recent version if you prefer, and if more recent versions are in the Ubuntu repository.

sudo apt install python3.10-venv

Download and unzip the Qualcoder folder.

Open a terminal and move (cd) into that folder. You should be inside the QualCoder-master folder or if using a release, e.g. the Qualcoder-3.5 folder. Inside the QualCoder-master folder:

python3.10 -m venv qualcoder

Activate venv, this changes the command prompt display using (brackets): (qualcoder) Note: To exit venv type deactivate

source qualcoder/bin/activate

• Update pip so that it installs the most recent Python packages.

pip install --upgrade pip

• Install the needed Python modules.

pip install chardet ebooklib ply openpyxl pandas pdfminer pyqt6 pillow pdfminer.six plotly pydub python-vlc rispy six SpeechRecognition

• You must be in the QualCoder-master folder (Or the main release folder if using a release. e.g. QualCoder-3.5 folder). Install QualCoder, and type the following, the dot is important:

python3 -m pip install .

You may get a warning which can be ignored: WARNING: Building wheel for Qualcoder failed

• To run type

After all this is done, you can deactivate to exit the virtual environment. At any time to start QualCoder in the virtual environment, cd to the Qualcoder-master (or Qualcoder release folder), then type: source qualcoder/bin/activate Then type qualcoder

Arch/Manjaro Linux

It has not been tested, but please see the above instructions to build QualCoder inside a virtual environment. The below installation instructions may affect system-installed python modules.

• Install modules from the command line

sudo pacman -S python python-chardet python-openpyxl python-pdfminer python-pandas python-pillow python-ply python-pyqt6 python-pip

• Install additional python modules

sudo python3 -m pip install ebooklib plotly pydub python-vlc rispy SpeechRecognition

If successful, all requirements are satisfied.

• Build and install QualCoder, from the downloaded folder type

sudo python setup.py install

• To run type:

Or install from AUR as follows:

yay -S qualcoder

Fedora/CentOS/RHEL linux

It has not been tested, but please see the above instructions to build QualCoder inside a virtual environment. The below installation instructions may affect system-installed Python modules.

Retrieve the current package code from this repository

• Open your preferred shell (terminal).
• Navigate to your preferred code directory.
• There, run: git clone https://github.com/ccbogel/QualCoder.git and
• enter the directory with cd QualCoder
• Make install_fedora.sh executable ( chmod +x install_fedora.sh ) and
• run the ./install_fedora.sh script from the terminal. The script is for Python version 3.11.

Then start QualCoder as any other app on your system.

Note 1_ This script installs the dependencies using dnf and the ebook libraries with a work-around, specified at #72 (comment) .

Note 2: Fedora uses Wayland which does not work well with the Qt graphical interface (for now). I suggest you also install Xwayland.

The instructions work on Mac Monterey. It is recommended to use a virtual environment, see: https://sourabhbajaj.com/mac-setup/Python/virtualenv.html The below instructions can be used inside a virtual environment folder instead of placed in Applications.

You will need to install developer tools for macOS. See https://www.cnet.com/tech/computing/install-command-line-developer-tools-in-os-x/

Install recent versions of Python3 and VLC .

Download the latest release "Source code" version in ZIP format, from the releases section of the project here on Github: https://github.com/ccbogel/QualCoder/releases/tag/3.5 and extract it into /Applications

Open the Terminal app (or any other command shell)

Install PIP using these commands (if not already installed). Check pip is installed: try typing pip3 --version and hit ENTER)

-> You should now be able to run pip3 as above.

• Install Python dependency modules using pip :

Be sure that you are in the QualCoder-Master directory before doing Step 6.

To change the directory, enter or copy and run the script below.

cd /Applications/QualCoder-3.5

• From the QualCoder-Master directory run the setup script:

Assuming you downloaded the 3.5 version. You can now run with:

Alternative commands to run QualCoder (Suggestions):

From any directory:

From the QualCoder-Master directory:

python3 -m qualcoder

python3 qualcoder/__main__.py

You can install QualCoder anywhere you want, so the path above depends on where you extracted the archive.

Another option to run Qualcoder is shown here: https://www.maketecheasier.com/run-python-script-in-mac/ . This means you can right-click on the qualcoder.py file and open with --> python launcher. You can make an alias to the file and place it on your desktop.

Another option to install on Mac:

• Open the Terminal App and move to the unzipped Qualcoder-Master directory, then run the following commands:
• Install Python dependency modules using pip3 :

pip3 install chardet ebooklib ffmpeg-python pyqt6 pillow ply pdfminer.six openpyxl pandas plotly pydub python-vlc rispy six SpeechRecognition

pip3 install -U py2app or for a system installation of python sudo pip3 install -U py2app

python3 setup.py py2app

Dependencies

Python 3.8+ version, pyqt6, Pillow, six (Mac OS), ebooklib, ply, chardet, pdfminer.six, openpyxl, pandas, plotly, pydub, python-vlc, rispy, SpeechRecognition

QualCoder is distributed under the MIT LICENSE.

Citation APA style

Curtain, C. (2023) QualCoder 3.5 [Computer software]. Retrieved from https://github.com/ccbogel/QualCoder/releases/tag/3.5

Dr. Colin Curtain BPharm GradDipComp Ph.D. Pharmacy lecturer at the University of Tasmania. I obtained a Graduate Diploma in Computing in 2011. I have developed my Python programming skills from this time onwards. The QualCoder project originated from my use of RQDA during my PhD - Evaluation of clinical decision support provided by medication review software . My original and now completely deprecated PyQDA software on PyPI was my first attempt at creating qualitative software. The reason for creating the software was that during my PhD RQDA did not always install or work well for me, but I did realise that I could use the same SQLite database and access it with Python. The current database is different from the older RQDA version. This is an ongoing hobby project, perhaps a labour of love, which I utilize with some of the Masters's and Ph.D. students I supervise. I do most of my programming on Ubuntu using the PyCharm editor, and I do a small amount of testing on Windows. I do not have a Mac or other operating system to check how well the software works regards installation and usage.

https://www.utas.edu.au/profiles/staff/umore/colin-curtain

https://scholar.google.com/citations?user=KTMRMWoAAAAJ&hl=en

Leave a review

If you like QualCoder and find it useful for your work. Please leave a review on these sites:

https://www.saashub.com/qualcoder-alternatives

https://alternativeto.net/software/qualcoder

Also, if you like Qualcoder a lot and want to advertise interest in its use, please write an article about your experience using QualCoder.

Code of conduct

Releases 19, contributors 30.

• Python 100.0%

Digital Humanities

• Getting Started
• Digital Scholarship Services
• Mapping and Timelines
• Storytelling
• Text Analysis
• Visualization
• Static Sites & Minimal Computing
• Communities

Text Corpora

• Text Data Mining by Nick Wolf Last Updated Jun 4, 2024 2050 views this year
• Google N-Grams Viewer Designed by Google which allows a user to plot the use of words over time in approx. 5 million books. You can select sets of curated works: "American English," "British English," "English," "Chinese (simplified)," "English Fiction," "French," "German," "Hebrew," "Russian," and "Spanish."
• HathiTrust Research Center The HathiTrust Research Center (HTRC) facilitates non-profit and educational uses of the HathiTrust Digital Library by enabling computational analysis of public domain works and (on limited terms) in-copyright works from its collection.
• Time Magazine Corpus Allows you to search more than 100 million words of text of American English from 1923 to the present, as found in TIME magazine.

Open E-Book Collections

• Directory of Open Access Books A continuously updated collection of academic peer-reviewed books from over 50 publishers.
• Google Books Search a large index of the world's books. Find millions of great books you can preview or read for free.
• Hathi Trust HathiTrust Digital Library is a large-scale collaborative repository of digital content from research libraries including content digitized via Google Books and the Internet Archive digitization initiatives, as well as content digitized locally by libraries. 17+ million digitized items.
• Internet Archive Internet Archive is a non-profit digital library offering free universal access to books, movies & music, as well as 376 billion archived web pages.
• Project Gutenberg Project Gutenberg offers over 42,000 free ebooks from books in the public domain: choose among free epub books, free kindle books, download them or read them online.

Text Visualization Tools

• Bookworm Bookworm is a simple and powerful way to visualize trends in repositories of digitized texts.

You can select sets of curated works: "American English," "British English," "English," "Chinese (simplified)," "English Fiction," "French," "German," "Hebrew," "Russian," and "Spanish."

Text Mining Tools

• Calibre Calibre is a free and open source e-book library management application developed by users of e-books for users of e-books. It has a cornucopia of features including E-book format conversion.
• HathiTrust Research Center The HathiTrust Research Center enables computational access for nonprofit and educational users to published works in the public domain and, in the future, on limited terms to works in-copyright from the HathiTrust.
• Hermeneutica A collection of text mining tools accompanied by sets of text (an online book) describing the how’s and why’s of text mining in modern scholarship. Hermeneuti is a collaborative project by Stéfan Sinclair & Geoffrey Rockwell.
• Voyant Tools Voyant Tools is a web-based text reading and analysis environment. It is a scholarly project that is designed to facilitate reading and interpretive practices for digital humanities students and scholars as well as for the general public. The site includes example projects. You can add text directly into the Voyant browser app and create interactive panels to embed in your online publications.

Text and Video Annotation

• Annotation Studio Annotation Studio is a suite of collaborative web-based annotation tools currently under development at MIT.
• Hypothes.is An open-source software project that aims to collect comments about statements made in any web-accessible content, and filter and rank those comments to assess each statement's credibility. It has been summarized as "a peer review layer for the entire Internet."
• Manifold The intuitive, collaborative, open-source platform for scholarly publishing that allows for deep reading, annotation, and engagement. You can create books or engage with others published in this platform.
• Thing Link Seamlessly make your images, videos, and 360 content interactive with text, links, images, videos and over 70 call to actions, creating memorable experiences for audiences.
• VideoAnt VideoAnt is a web-based video annotation tool for mobile and desktop devices. Use VideoAnt to add annotations, or comments, to web-hosted videos. VideoAnt-annotated videos are called “Ants”.
• << Previous: Storytelling
• Next: Visualization >>
• Last Updated: Aug 7, 2024 2:26 PM
• URL: https://guides.nyu.edu/digital-humanities

Home / Text Analysis Software

Updated on: August 16, 2024

Text Analysis Software List (August 2024)

Microsoft Text Analytics API

Turn unstructured text into meaningful insights with Text Analytics. Get sentiment analysis, key phrase extraction, and language and entity detection.

IBM Watson Explorer

IBM Watson Explorer is a new way to explore all your unstructured data. It uses text analytics and machine learning to help you find secrets hiding in your data. A complex claim that took 2 days to process can now be completed in 10 minutes.

SAS Contextual Analysis

Uncover insights hidden in massive volumes of textual data with SAS Visual Text Analytics, which combines powerful natural language processing, machine learning and linguistic rules to help you get the most out of unstructured data.

SAS Sentiment Analysis

SAS Visual Text Analytics

Micro Focus IDOL

Micro Focus IDOL enterprise search & data analytics platform searches & analyzes unstructured data from any source including text, video & speech. Learn more here.

Enago Reports

Enago Reports is the ultimate solution for those seeking to produce professional writing of the highest standards. This service’s comprehensive set of features helps simplify the writing and proofreading process while ensuring compliance and accuracy. When they use Enago Reports, they can be sure of superior language quality in their work. These AI-powered native language editors review, edit, and proofread their documents to check grammar, punctuation, and spelling. These native editors also ensure that their writing is culturally appropriate for their target audience. Enago Reports provides a reliable source of information to eliminate bias in their work. These editors employ a systematic approach to gathering and structuring information to help support their argument. They also use AI technology to identify any potential bias during the review process. The result? Well-researched, unbiased work that’s sure to impress any reader. Enago Reports can help them sidestep the dreaded process of proofreading and journal submission. These automated proofreaders scan each document for mistakes faster than ever before. This submission wizard helps them optimize their article for journal acceptance, so they can put themselves one step ahead of the competition. Lest we forget, Enago Reports can protect them from plagiarism and AI-generated content. This advanced software scans for any copied content and flags it for their review. This helps them avoid any possible legal issues that may arise from copying somebody else’s work. So why settle for less? Choose Enago Reports and produce professional work that is free of bias and errors. Put their trust in us for superior language quality, technical compliance, and a one-stop solution for all their writing needs.

Wootric Text & Sentiment Analytics

Wootric CXInsight™ uses machine learning to auto-categorize and assignment sentiment to unstructured feedback from surveys, online reviews, social media, support tickets, employee feedback, and more. Machine learning identifies category themes and sentiment in each verbatim comment, instantly analyzing volumes of feedback. No more silos — democratize customer data.

Reduce churn and increase revenue with Lumoa's customer experience platform. Understand what drives customer experience in an easy and affordable manner. Collect, analyze, control and take actions on customer feedback.

Undetectable AI

Undetectable AI understands this struggle and has created a solution that will revolutionize the world of content creation. This powerful AI Detector and AI humanizer were specifically designed with serious writers and content creators in mind. They believe that every piece of content should have its unique voice and perspective, and with Undetectable AI, they can achieve just that. This AI Detector is a game-changer in itself. Unlike other detectors that simply highlight common phrases and sentences, ours goes a step further. It thoroughly scans its content and suggests changes to make it more original and distinctive. With just a click of a button, they can instantly enhance the quality of their writing. But what truly sets us apart from the rest is our AI humanizer. They understand that while AI can help with efficiency, nothing beats the creativity and emotion a human can bring to the table. That's why our humanizer allows them to humanize their AI text with consistency and quality. Their content will have the perfect balance of both AI and human elements, making it stand out from the rest. With Undetectable AI, they can be confident that their content will be top-notch, professional, and above all, undetectable as AI-generated.

Provalis Research QDA Miner

QDA Miner is an easy-to-use quantitative data analysis software package. QDA Miner can analyze small and large collections of documents and images.

Canvs AI is an emotion and behavior insights platform that understands how consumers feel, why they feel that way, and the business impact those feelings and behaviors create for brands through analyzing digital conversations, such as open ended text prompts in surveys, online product reviews, or from social comments on Twitter, Facebook, Instagram and YouTube.

Luminoso Daylight

Luminoso Daylight is a cloud-based Natural Language Understanding platform for sentiment analysis in 15 languages. The software offers tools to analyze and report on the presence of sentiment in text data. Evaluate mixed datasets with correlating text feedback and numeric ratings. Visualize concepts relate to each other and identify themes.

PoolParty Semantic Suite is the most complete and advanced semantic middleware platform on the global market. It uses innovative means to help organizations build and manage enterprise knowledge graphs as a basis for their AI strategy. A knowledge graph is a rich representation of a knowledge domain that is capable of deriving more understanding out of your data. It is used to break down data silos by linking data from various departments and organizations.

Quantexa is an integrated Contextual Decision Intelligence (CDI) platform used to connect data points to create context – entity graphs enriched with vital intelligence. Analytics can be powered by context and embedded into operational decision processes. It offers a modular integrated platform with open data interfaces and APIs.

Fractal Analytics helps global Fortune 100 companies power every human decision in the enterprise by bringing analytics and AI to the decision.

Citibeats is an artificial intelligence platform used to search and analyze the large amounts of text provided by citizens. The software offers tools to identify social facts and trends that are useful for companies and institutions. Interpret the needs and opinions of users in real-time on a large scale.

Indico Data

Indico Data is a platform used to drive more value across the business. The software offers tools capable of handling even the most unruly, unstructured intake data. Take documents from any source and transform them into insights and competitive advantage. Integrated OCR with built-in quality correction to measure the performance of work-with technologists.

Lang.ai is a no-code service automation platform that empowers customer support teams to build AI models that they can directly control to improve and automate critical support processes. We seamlessly integrate into Zendesk and Salesforce and take the tedious and manual tasks out of agents’ hands so they can focus on what is most important, the customer. Our customers are leveraging Lang for the following use cases and with our plug and play technology they’re up and running in 48 hours with no model training and maintenance required.

Moderation API

Modern businesses demand quick, accurate content moderation. With the Moderation API from [Company Name], they can automate text analysis to streamline their workflow. Utilizing state-of-the-art artificial intelligence technology, this API helps professional teams become more efficient and save time without compromising on privacy and accuracy. The Moderation API ensures that the scales are faster and stays ahead of the competition. Working with this revolutionary tool, companies can trust that their content is moderated quickly without sacrificing quality or safety standards. With robust and reliable tools such as these, growing businesses can trust that their content is in good hands. Choose the Moderation API to ensure the business's success.

Text analysis software uses natural language processing (NLP) to extract valuable insights from structured and unstructured text data. This tool enables professionals to understand the sentiment analysis, key phrases, language nuances, recurring themes, and patterns that emerge from the data. Text Analytics software can process data from various channels, including emails, phone call transcripts, surveys, and customer feedback. Additionally, it can integrate with other analytical platforms, such as big data analytics and business intelligence systems. To qualify as text analysis software, a product must import text data from different sources and offer visualization features for text data analysis.

List of Text Analysis Software

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• Open access
• Published: 14 August 2024

Financial hardship screening among Native American patients with cancer: a qualitative analysis

• Amber S. Anderson-Buettner 1 ,
• Amanda E. Janitz 1 ,
• Mark P. Doescher 2 ,
• Stefanie D. Madison 3 ,
• Michaela A. Khoussine 4 ,
• Keri L. Harjo 5 ,
• Marvin B. Bear 6 ,
• Stephnie Dartez 7 ,
• Sheryl K. Buckner 8 &
• Dorothy A. Rhoades 9  

BMC Health Services Research volume  24 , Article number:  928 ( 2024 ) Cite this article

Metrics details

Cancer-related financial hardship is an increasingly recognized concern for patients, families, and caregivers. Many Native American (NA) patients are at increased risk for cancer-related financial hardship due to high prevalence of low income, medical comorbidity, and lack of private health insurance. However, financial hardship screening (FHS) implementation for NA patients with cancer has not been reported. The objective of this study is to explore facilitators and barriers to FHS implementation for NA patients.

We conducted key informant interviews with NA patients with cancer and with clinical staff at an academic cancer center. Included patients had a confirmed diagnosis of cancer and were referred to the cancer center through the Indian Health Service, Tribal health program, or Urban Indian health program. Interviews included questions regarding current financial hardship, experiences in discussing financial hardship with the cancer care and primary care teams, and acceptability of completing a financial hardship screening tool at the cancer center. Clinical staff included physicians, advanced practice providers, and social workers. Interviews focused on confidence, comfort, and experience in discussing financial hardship with patients. Recorded interviews were transcribed and thematically analyzed using MAXQDA® software.

We interviewed seven patients and four clinical staff. Themes from the interviews included: 1) existing resources and support services; 2) challenges, gaps in services, and barriers to care; 3) nuances of NA cancer care; and 4) opportunities for improved care and resources. Patients identified financial challenges to receiving cancer care including transportation, lodging, food insecurity, and utility expenses. Patients were willing to complete a FHS tool, but indicated this tool should be short and not intrusive of the patient’s financial information. Clinical staff described discomfort in discussing financial hardship with patients, primarily due to a lack of training and knowledge about resources to support patients. Having designated staff familiar with I/T/U systems was helpful, but perspectives differed regarding who should administer FHS.

Conclusions

We identified facilitators and barriers to implementing FHS for NA patients with cancer at both the patient and clinician levels. Findings suggest clear organizational structures and processes are needed for financial hardship to be addressed effectively.

Peer Review reports

Introduction

Cancer-related financial hardship is an increasingly recognized problem for patients, families, and caregivers [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. As a relatively new field of study, financial hardship screening among patients with cancer is being studied in diverse settings with diverse instruments [ 9 ]. In a recent study, researchers looked at both medical and nonmedical financial hardship and sacrifice among patients with cancer in the general, all-races US population. Medical financial hardship included domains such as material (bankruptcy), psychological (stress related to paying bills), and behavioral (delaying care due to cost) whereas nonmedical financial sacrifice contained categories like savings depletion and changes in spending. More than 38% of study participants had to make financial sacrifices and 42% reported medical financial hardship [ 6 ]. Other studies that focused on the association between cancer and financial hardship similarly discovered financial sacrifices resulting from cancer treatment [ 10 , 11 , 12 , 13 ]. Such hardship has been linked to limited care, worse treatment adherence and poor health outcomes [ 7 , 14 , 15 ].

Financial hardship may be worse in some racial minority populations than in the general population [ 6 , 16 , 17 , 18 ], but cancer-related financial hardship for Native American (NA) patients has rarely been reported. Many NA patients, including persons of American Indian (AI) or Alaska Native descent, may be at increased risk for cancer-related financial hardship due to highly prevalent factors, such as low income, medical comorbidity [ 19 ], and lack of private health insurance coverage. Although a few studies of NA patients with cancer included a measure of anxiety or stress due to costs of care [ 20 , 21 ], financial hardship was not the focus of these studies.

Further, the systems of care utilized by many NA patients are unique, have a complex history, and often poorly understood [ 22 ]. NA patients are eligible to receive medical care coverage from an Indian Health Service (IHS) facility, a tribal health facility, or an IHS-supported urban health program, collectively abbreviated as “I/T/U” programs [ 23 , 24 ]. The IHS is a federal program that operates a network of hospitals and clinics in the United States (US). Tribal Health Program hospitals and clinics are operated by individual tribes, which also receive federal (IHS) funding to care for NA patients. Urban Indian Programs are independent organizations that receive IHS funds for NA patients. Marked differences in coverage exist between I/T/U programs, which also differ from other payers. Most I/T/U programs have limited resources and are severely under-resourced for cancer care and must refer patients to outside entities [ 24 ]. Unfamiliarity with the I/T/U systems of care or payment at the outside entities can lead to confusion and delays in oncologic referrals and treatment for many NA patients.

Although studies have evaluated financial hardship among NA patients with cancer, no study of implementing a FHS tool among NA patients with cancer exists in the current literature. NA patients with cancer in one study often reported financial barriers to care [ 20 ] as well as lack of coordination between systems of care as a major barrier to care [ 20 , 25 ]. Male caregivers of patients with cancer on a reservation in the American Southwest more often identified financial burden of caregiving as their leading concern than did female caregivers [ 26 ].

Potential facilitators and challenges to screening for cancer-related financial hardship for I/T/U patients have not been previously studied. Studies of cancer-related financial hardship in other populations may not reflect those faced by patients who depend on I/T/U systems of care for coverage.

The I/T/U system in Oklahoma includes approximately 50 federally operated health centers or hospitals, tribally operated health centers or hospitals, and urban outpatient facilities. Oklahoma has the largest tribal land area in the US. At the time of the study, more than 14% of citizens (~ 483,000) identified as NA either alone or in combination with another race, the highest proportion in the US [ 27 ]. Incidence and mortality rates from high-priority cancers are especially alarming for the state’s NA population, with 1.4 and 1.8 times higher mortality for lung and colorectal cancer, respectively, compared with the non-Hispanic White state population [ 28 ].

To design and implement a pilot FHS program for NA patients referred to a cancer center in Oklahoma, we obtained stakeholder views on facilitators and challenges to FHS. This manuscript includes findings from the first objective of a three-part study. Findings from subsequent parts of the study, including pilot FHS implementation and evaluation, will be presented elsewhere. We hypothesized that themes specific to NA patients referred by I/T/U facilities would emerge that inform the design and implementation of FHS for these patients. We also hypothesized that designated referral coordinators, or navigators, for I/T/U patients at the cancer center would be considered as helpful initiators for FHS. This study reports findings from patient, clinical provider, and clinical staff key informant interviews that identify several facilitators and challenges to consider when designing FHS. The results support FHS that considers facilitators and challenges related to I/T/U systems of care, as well as the role of designated staff familiar with these systems.

This descriptive case study research design uses qualitative methodology and a stakeholder-engaged approach [ 29 , 30 , 31 ] with semi-structured interviews to gather diverse perspectives related to FHS implementation for NA patients. This approach helps identify specific contextual facilitators and barriers to implementation that may not have been identified in other healthcare settings.

The setting for the study is the Stephenson Cancer Center (SCC), affiliated with the University of Oklahoma Health Sciences, opened in 2011. In recognition of the need for expertise in I/T/U health systems SCC created the American Indian Navigation Program (AINP). Of 20,540 patients at SCC in 2019, NA patients accounted for 6.4%, with more than half referred from I/T/U facilities across the state. From 2017–2019, 1,222 new I/T/U patients from multiple federally recognized tribal nations received AINP services. The American Indian Navigators (AINs) see any patient referred from an I/T/U facility, regardless of the type of cancer diagnosis.

The interview guides were developed for this study. In February 2021, the research team met with a 10-person stakeholder advisory board consisting of I/T/U and SCC clinicians, staff, and a NA patient for feedback regarding a semi-structured, open-ended instrument to guide patient (Patient Interview Guide) and provider (Provider Interview Guide) interviews. The Consolidated Framework for Implementation Research (CFIR) [ 32 ] served as the conceptual model for the guide to ensure consideration of factors that may influence financial hardship screening for NA patients with cancer [ 33 ]. The CFIR includes five domains (intervention characteristics, outer setting, inner setting, characteristics of individuals, and process) that may affect the implementation of a new screening process.

Key informants for semi-structured interviews included NA patients as well as clinic providers and staff at SCC. We used purposive sampling to recruit within the identified clinics as both patients and providers. Signed informed consent was obtained for all participants.

Eligibility for patients to participate as key informants included being NA, referred from an I/T/U system, ages 18 years or older, diagnosed with cancer, and currently receiving cancer care at the SCC. Project staff reviewed patient lists of potentially eligible patients and advised on which patients to recruit for this project based on general health status and reason for the visit (e.g., standard follow-up or chemotherapy visit). Patient participants completed a brief questionnaire immediately following the interview to collect demographic and clinical information, including cancer diagnosis and date of diagnosis as well as gender, race, ethnicity, age at diagnosis, highest level of education, and household income.

Eligibility for SCC personnel included being a provider (physician, physician’s associate, nurse practitioner) or clinical or support staff (clinic or nurse manager, case manager, social worker) at an SCC clinic. Providers were recruited through all-staff emails sent to SCC staff. SCC providers and staff completed a brief questionnaire to include demographics (gender, race, ethnicity, and age), years in practice, and their roles at SCC.

All patient and provider/staff participants were individually interviewed either in-person or by teleconference (Zoom) in sessions lasting less than one hour. Interviews were digitally audio-recorded and transcribed. Transcribed interviews were checked for accuracy and uploaded into MAXQDA for analysis [ 34 ]. Two research team members reviewed the transcripts, developed the codebooks, and coded interviews together to ensure cultural nuances were captured. Independent codebooks for provider and patient interviews were developed and revised. To enhance rigor and reproducibility, codes were developed and revised through an iterative process. Integrated approaches of inductive and deductive analysis have been shown to provide a more comprehensive perspective of the phenomenon of interest [ 35 ]. Codes were first developed using the key informant interview guides and subsequently revised as relevant patterns emerged. Through routine discussion, agreement of coding structure and definitions was achieved. Codes were reviewed and patterns were identified in each data set, presented below as themes. Findings from the analysis were shared with the research team and Stakeholder Advisory Board.

As noted, eight patients consented to the interview, and seven (four female, three male) patients completed the interview. Three patients were aged 35–54 years and four were 65 years and older. Four participants had lung cancer, and the three others had different cancers. Four were currently in treatment for their cancer, with two determining the treatment plan and one obtaining follow-up care from initial surgery. Provider and staff participants varied in position and included two Physician Assistants, one Social Worker, and one Surgical Oncologist. Years of practice ranged from four to 16 years.

Themes identified for both providers/staff (herein simplified to “providers”) and patients included: 1) existing resources and support services; 2) challenges, gaps in services, and barriers to care; 3) nuances of American Indian cancer care; and 4) opportunities for improved care and resources.

Existing resources and support services

Providers reported complexities in coordinating care for the NA patients. Interfacing with I/T/U health systems was identified as a challenge, particularly the inability to electronically prescribe medications, requirement to have certain orders completed at the referring facility (e.g., CT scans completed at the I/T/U facility instead of the cancer center) and obtaining pre-authorization for certain procedures to be completed at the cancer center. Providers perceived that the pre-authorization process delays care in some cases. While requiring patients to have orders completed by their referring I/T/U is also perceived as a barrier to some providers, others reported that this requirement benefited some patients by not having to make multiple trips to the cancer center. To reduce patient travel, some providers reported strategically scheduling multiple appointments on the same day.

Patients reported that their respective I/T/U provided support in a variety of capacities, including direct financial support with the amount and availability of support varying by site. Other types of support provided to participants by I/T/Us included gift cards to purchase gasoline for their vehicle, housing and utility assistance, and food assistance. In addition to financial support, some participants reported that their I/T/U provided support with their cancer care coordination by navigating the referral process, explaining the treatment plan, and prioritizing patients with cancer so that services and referrals can be completed in a timely manner. Other participants had to rely on alternative, pre-existing sources of funding and insurance to support themselves during treatment, such as Veterans Affairs, Medicare, Medicaid, or Social Security to make ends meet. Other sources of financial support reported by participants included personal savings accounts, unemployment, and tribal resources.

Patients identified resources to aid in the care coordination process, including navigation assistance at the cancer center, the I/T/U facility, and family support. Some patients relied solely on cancer center navigation services, whereas others received additional care coordination services at their referring I/T/U. One patient had received a medical bill from the cancer center but after sending to their I/T/U representative, the bill was paid in full, and the patient’s burden was alleviated. Regarding family support, most patients reported having a strong support system, relying on spouses, children, grandchildren, and parents to alleviate burdens related to their cancer care. Family support included providing transportation to cancer treatment, advocating for patient needs, coordinating cancer care, and identifying financial resources. Some patients described their family support systems more broadly, whereas others identified specific instances where family had assisted. One patient recounted the support their spouse provided, indicating that their life was saved as a result of them encouraging the patient to schedule an initial appointment. Participants also reported that their families provided financial support, assisting in several capacities ranging from bills to their children’s school uniforms.

Challenges, gaps in services, and barriers to care

All providers reported familiarity with the cancer center’s AINP and described the AINs as helpful for both providers and patients and critical mediators between I/T/Us and the cancer center. Although providers perceived the AINs to be communicative, some expressed a desire to have them become more accessible and embedded into their clinic. Providers felt that the limited number of AINs was a potential barrier to expanding navigation services, including financial hardship screening. Several barriers to treating NA patients were reported by providers. Staffing limitations have resulted in the inability to implement screening tools, embed necessary staff within the clinic, or adequately address financial challenges faced by patients. Providers noted the need for better clinical integration of social workers and financial navigators and suggested that having a point of contact within social work would promote care coordination and relationship building between patients and clinical staff. Challenges related to tribal health referrals were also identified as a barrier to care. Providers described challenges in discussing financial hardship with patients. Given the variety of roles among providers, the degree of comfort in discussing financial hardship varied. Providers who routinely refer patients to resources considered financial-related conversations to be part of their job responsibility, whereas those directly involved in medical care experienced discomfort in having these conversations (Table  1 ).

Providers reported that some NA patients experienced reduced quality of care, compared to other patients, due to the unique health system features as previously described (inability to electronically prescribe certain medications, requirement of imaging and/or lab tests to be completed at the I/T/U facility), and the mandatory pre-authorization process. A major challenge reported by multiple providers was transportation, with resources including transportation services for low-income participants through Medicaid, gas reimbursement using small emergency funds, or charitable donations. Other patient challenges included maintaining employment and few resources for childcare.

Patients identified multiple financial challenges to their cancer treatment, primarily related to logistics, such as transportation, lodging, and food during travel to the cancer center. The AIN team and I/T/Us could provide resources for some patients, including gas cards, hotel vouchers, and support for housing, utilities, and children. However, the I/T/Us primarily provided these resources, which were nevertheless limited, with some participants noting that there were no funds available when they inquired about them. Participants frequently relied on a family member for transportation to the cancer center, adding stress for the participants in arranging consistent transportation. While some transportation options are available through Medicaid, some patients lived outside of the radius for this transportation service.

Multiple patients reported unanticipated challenges during their cancer journey and concerns related to the financial impacts on their families. These included not knowing what to expect of cancer treatment after a recent diagnosis, transportation, and coping with changes to physical appearance after cancer-related surgery. The COVID-19 pandemic was also noted by multiple participants as affecting their ability to attend appointments and being generally disruptive.

Multiple patients reported psychosocial stress, including transportation concerns, stresses in their work environment, food insecurity, having enough funds to care for family and required medical treatment. Resources that patients described included Family Medical Leave Act, family support, and tribal resources (though these were limited). Patients reported that they relied on their faith and their family for support during stressful situations, like cancer diagnosis and treatment. Some patients reported stress associated with a cancer diagnosis, side effects of their cancer treatment, and challenges with pre-existing health conditions. Participants also identified challenges related to the management of taking new and numerous medications, pre-existing mobility, and vision impairments, and maintaining the right state of mind at work.

Nuances of Native American cancer care

Providers reported several nuances in treating and providing services to their NA patients. The logistics in care coordination between I/T/U systems and SCC were described as a challenge by some providers. Other challenges acknowledged by providers included lack of accommodations and travel support for long distances required for patients to travel for cancer care. Some providers had misconceptions of resources available to NA patients, with some believing that NA patients have more resources, whereas others believed they have fewer resources. One participant reported that although some NA patients may have access to unique resources for healthcare support, they face unique system-related challenges, including a complex referral and authorization process, as described further below (Table  2 ).

One patient noted cultural nuances and perspectives of cancer care. This participant emphasized the importance of returning to traditional ways and communal benefit when completing the interview by stating, “You don’t look out for one. You got to look out as a whole.” The value of family and community involvement and support was also described as an important component of care, stating their spouse answered healthcare providers’ questions because they are “just really Indian.” Other patients did not note specific nuances they attributed to NA differences in health care systems.

Opportunities for improved care and resources

Providers described several opportunities for improvement to better address the needs of NA patients. While some providers were less familiar with current financial hardship screening protocols, most participants indicated that screening strategies needed to be enhanced and better integrated into clinical workflow. Providers noted that financial hardship extends beyond the inability to pay medical bills; therefore, screening should also include food insecurity, transportation barriers, and challenges in filling prescriptions. Some participants expressed a desire to be more knowledgeable about anticipated financial hardships and resources for patients, in addition to having increased access to social workers.

Discomfort in discussing financial hardship was reported to be a result of the limited knowledge of resources and lack of adequate training in having financial-related conversations. Providers were not consistent in addressing FHS with all patients but would discuss if the patient asked a question. Providers reported that AINs and social workers were better equipped to aid patients with financial concerns. However, most providers expressed interest in training related to financial hardship screening, including skills for administering screening tools, resources available for patients, and awareness of patient needs (Table  3 ).

Providers had differing perspectives of patients’ comfort in discussing financial hardship, although some providers recognized that each patient is unique in their willingness to discuss their finances. According to some providers, some patients are upfront about their financial needs whereas others are willing to discuss challenges only if inquired. Providers suggested standardizing the FHS process so that patients are screened as a result of protocol, rather than after a patient expresses a concern. Providers recommended FHS that is thorough and specific yet brief enough for patients to complete routine paperwork. Opinions about who should conduct FHS varied among providers. Clinicians stated they rely on other staff (like social work or navigators) to address financial issues, unless the financial issue is obvious (e.g., missed appointments). Providers reported that, ideally, FHS would occur with the patient’s primary care provider, but many patients do not receive consistent primary care.

Participants stated that incorporating FHS into the standard EMR workflow, including automatic referrals and routine screening, would allow FHS to fit well within the current distress/social work referral process. FHS should be done routinely (e.g., every 6 months). Providers consistently stated that the first appointment with the SCC team for a newly diagnosed cancer was not the ideal time to address financial hardship due to the overwhelming nature of a cancer diagnosis. Ideally, AINs, social work, and nurse/financial navigators would be more accessible to address financial concerns as they arise.

Patients reported differing perspectives of financial hardship discussions and screening. While some patients indicated that someone on their healthcare team should be aware of financial challenges patients experience, others stated that inquiring about financial situations could feel like prying if resources were not available to address challenges. Among those who were comfortable with screening, some patients stated no preference of length as long as questions were not repetitive, whereas others preferred a shorter length – one page, 8–20 questions, or 1–15 questions. There were also differing preferences on when and how frequently the screening tool should be implemented. Some patients indicated that the screening tool should be completed at the initial diagnosis so that financial challenges are identified early in the process, whereas others reported that screening should occur after a treatment plan has been established due to the unexpected nature of many emerging financial challenges. Patients reported differing preferences on how the screening was completed, with some preferring in-person at their appointment and others over the phone. Generally, participants reported few financial related topics to avoid in screening for financial hardship, however discussions related to savings, credit standing, and long-term financial situation should be avoided. Some patients also identified a specific member of their healthcare team who they would feel most comfortable having financial related conversations with, including their provider or AIN. Some patients reported that resources are challenging to identify at times and having their healthcare team screen for anticipated challenges would reduce financial distress.

Most patients reported that their healthcare team had not discussed financial related challenges in the past, although resources such as transportation assistance and lodging had been offered. Some patients reported that they had not discussed financial hardship related to their cancer care with their I/T/U or referring provider, however some were informed that their treatment would be paid for by their referring I/T/U or that an AIN at the cancer center would assist with financial challenges that may arise. Patients were inconsistent in their perceptions of their healthcare team serving as a resource. Some patients reported that their healthcare team could provide financial resources, including gift cards, whereas others indicated that financial support services were not available for patients. All patients reported either working with an AIN or being willing to work with one to aid in care coordination processes. Additionally, the interactions with AINs ranged from having no recollection of working with them to routine interactions. Some patients indicated that having an AIN was a helpful resource, citing their role in securing lodging and transportation assistance during their cancer treatment. However, others were unaware of AIN services or confused about their role. Some unfamiliarity with AINs was attributed to family members serving as a liaison between the providers and the patient. Patients who were unaware of AIN services often reported that they would like to be connected.

Limitations included a small sample size due to recruitment challenges which may limit generalizability to larger populations. Another limitation were clinical workflow constraints which may have resulted in inhibited responses. In addition, as the study was conducted at a single cancer center, its findings may not be generalizable to all healthcare settings serving Native American patients.

Using a stakeholder approach for perspectives on implementing FHS for NA patients with cancer at an academic cancer center has revealed challenges and opportunities for both the cancer center and I/T/U systems. FHS was considered by both patients and providers as potentially beneficial to patients, as NA cancer care-related issues generally revolve around the different systems of care between I/T/U and non-I/T/U facilities. Having a designated team of navigators with expertise in the unique requirements of I/T/U patients was considered beneficial to both patients and providers. The need for more AINs was identified, especially if adding FHS to their roles. Structural systems to integrate FHS with both cancer center and I/T/U resources to address potential needs were viewed as potentially improving care by reducing delays in care or access to treatments. By integrating the CFIR framework within the interview guides, we ensured that each domain of CFIR was addressed, which will support development of future intervention strategies that account for each domain and provide the best opportunity for success. We included questions about future implementation of a financial hardship screening tool to assess comfortability and potential acceptability of questions related to financial hardship. By talking with both patients and providers, we were able to assess the outer setting (features of the external environment) and inner setting (organizational factors that may influence the intervention). We evaluated the characteristics of individuals by asking about their knowledge of financial hardship resources and financial needs during cancer treatment. By including questions of both patients and providers about what factors or strategies may influence implementation, we evaluated the process domain.

The reliance upon family was prominent and has emerged in other studies of NA patients with cancer, reporting that many NA families provide more than just emotional support [ 25 ]. While HIPAA presents challenges in the engagement of family, opportunities to expand involvement more systematically when requested by the patient exist.

While generally favorable to FHS, patients and providers differed in opinions regarding implementation. Training providers to undertake FHS and discuss financial concerns would be an opportunity to improve financial communication with patients. Adding FHS to the experience of receiving cancer care, however, may add to an often-overwhelming process. A study of NA patients found that excessive “paperwork” was a frequently noted barrier to cancer care [ 36 ]. The ideal implementation for asking and addressing financial barriers to care is currently being studied across the nation in diverse settings.

This study made identified several factors to consider when implementing FHS for NA patients referred to a cancer center. It employed a stakeholder approach to gather perspectives from both Native American cancer patients and cancer center providers on implementing FHS which allowed identification of facilitators and barriers to FHS implementation at both patient and provider levels. As hypothesized, specific I/T/U-related themes emerged, including both facilitators and challenges regarding coordination and coverage for care as well as varying degrees of cancer center and I/T/U support for indirect costs of care, such as transportation and lodging. Also as hypothesized, having AINs familiar with I/T/U systems of care were overall viewed by both patients and providers as useful in facilitating access to cancer work up and treatment, but additional staffing was identified as needed for implementation of FHS. Importantly, the study gathered culture-specific perspectives on the acceptability of FHS for Native American patients, addressing a gap in the literature. The application of the CFIR ensured a comprehensive assessment of factors influencing potential FHS implementation, providing a structured approach understanding the complex issues involved.

Theoretically, this research highlights the importance of considering cultural nuances and unique health system features when implementing FHS for Native American patients, emphasizing the need for culturally tailored approaches in healthcare interventions. The research demonstrated the utility of using implementation science frameworks like CFIR to guide assessment of FHS implementation factors, showcasing how such frameworks can be applied in real-world healthcare settings. Additionally, the study identified the need to balance FHS with existing screening burdens on cancer patients, pointing to the importance of considering the overall patient experience in healthcare interventions.

From a practical standpoint, the study offers several important insights. It suggests that clear organizational structures and processes are needed for financial hardship to be addressed effectively in healthcare settings. The findings indicate a need for provider training on conducting FHS and discussing financial concerns with patients, highlighting an area for potential improvement in patient care. The study also points to opportunities to better integrate American Indian Navigators and social workers into clinical workflows to address financial hardship. From a patient’s view, it highlights the need to understand the impact that family playsin FHS. Most importantly, this study suggests that FHS should be brief, routine, and integrated into existing clinical processes to be most effective and feasible.

A systematic review estimated that nearly half of individuals with cancer experience financial hardship [ 19 ]. It is associated with delays in cancer care and poor clinical outcomes [ 37 , 38 , 39 ]. The reasons for these adverse effects may reflect the multidimensional nature of financial hardship that encompasses: 1) direct consequences of treatment (e.g., out-of-pocket expenses, debt, and decreased income); 2) psychological distress because of costs; and 3) deleterious coping mechanisms (e.g., delaying or skipping medications or care) [ 40 ]. Because our study begins to address the important question of what financial hardship means for NA patients with cancer and their clinicians, our findings provide insights preparatory to future research to explore the underlying elements of financial hardship in relation to adverse health outcomes in NA patients.

In particular, our findings suggest that research on financial hardship among NA patients with cancer should examine the extent to which tribes and the I/T/U system can help address financial concerns, as the availability of tribal or I/T/U resources appears to be inconsistent. NA patients also emphasized the centrality of family and community in addressing financial issues, indicating that research examining the role of families in addressing financial hardship concerns is warranted.

A growing body of research suggests that implementation of navigation services for patients with cancer may help address financial hardship [ 41 , 42 , 43 ]. While some studies have examined NA-specific navigation programs [ 20 , 43 , 44 , 45 , 46 , 47 ], these studies have not focused on navigation that includes systematic financial hardship screening. Thus far, NA navigation programs have concentrated primarily on helping NA patients coordinate cancer care between oncology clinics and the I/T/U system. Successful NA navigation programs [ 20 , 43 , 44 , 45 , 46 , 47 ] are community-based and focus on patient needs, including barriers to accessing cancer care, cultural concerns, and education about cancer and treatment options [ 48 ]. Two studies have evaluated stress and anxiety due to the cost of cancer care among NA patients [ 20 , 21 ], but none included systematic financial hardship screening. Thus, research is needed to further investigate the potential impact and sustainability of navigated FHS interventions to help address financial concerns and enhance the care experience of NA patients with cancer. In particular, our findings suggest that research examining the implementation of systematic financial hardship screening should explore the questions of who should administer screening (e.g., navigators versus clinicians), how detailed the screening should be, and how frequently it should occur. Our findings also suggest that staffing limitations impede the ability to conduct financial hardship screening. Because the Centers for Medicare and Medicaid Services now provides coverage for cancer navigation services for persons with Medicare coverage [ 49 ], research could be conducted to determine whether this new CMS benefit can meaningfully support navigation services addressing financial hardship for NA patients with cancer.

This study had several limitations. Patient recruitment strategies interrupting clinical workflow and time restraints resulted in a small sample size for this study. We also experienced challenges in recruiting clinicians, likely due to clinical time restraints and unfamiliarity with FHS practices. While a broad range of NA perspectives were captured, tribal and cultural variance could be broadened in future studies. Conducting interviews with patients in a clinical environment may have inhibited their ability to adequately reflect and respond to the question guide. Similarly, provider interviews occurring outside of clinical hours may have allowed for enhanced responses. Future studies should examine culture-specific perspectives on the acceptability of FHS.

In conclusion, our study identified both facilitators and barriers to implementing FHS at a single cancer center among NA patients. Future studies should seek to understand the role of FHS in the context of the myriad of screenings cancer patients receive during treatment, in addition to the timing and frequency in which FHS should occur.

Availability of data and materials

Data are not available to be shared due to confidentiality issues.

Abbreviations

• Native American

Financial Hardship Screening

Indian Health Service (IHS) facility, a tribal health facility, or an IHS-supported urban health program

Stephenson Cancer Center

American Indian Navigation Program

American Indian Navigators

Consolidated Framework for Implementation Research

United States

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Acknowledgements

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Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number P30CA225520. Partial funding provided by National Institutes of Health, National Institute of General Medical Sciences, grant 1 U54GM104938. The content is solely the responsibility of the authors and does not necessarily represent the official view of the National Institutes of Health.

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Amber S. Anderson-Buettner & Amanda E. Janitz

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Mark P. Doescher

Oklahoma City Veterans Affairs Health Care System, Oklahoma City, OK, USA

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Little Axe Health Center, Absentee Shawnee Tribe, Norman, OK, USA

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ASAB analyzed and interpreted the data and drafted the initial manuscript. AEJ analyzed and interpreted the data and drafted the initial manuscript. MPD provided guidance on interpretation of data and contributed to writing the manuscript. SDM provided guidance on interpretation of data and contributed to writing the manuscript. MAK contributed to data collection. KLH contributed to data collection and provided guidance on interpretation of the data. MBB contributed to data collection and provided guidance on interpretation of the data. SD contributed to data collection and provided guidance on interpretation of the data. SKB contributed to writing the manuscript. DAR provided guidance on interpretation of data and contributed to writing the manuscript

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Anderson-Buettner, A.S., Janitz, A.E., Doescher, M.P. et al. Financial hardship screening among Native American patients with cancer: a qualitative analysis. BMC Health Serv Res 24 , 928 (2024). https://doi.org/10.1186/s12913-024-11357-6

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DOI : https://doi.org/10.1186/s12913-024-11357-6

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JSmol Viewer

Research on surface processing method of pulse transmission signal of amplitude-modulated drilling fluid in 10,000-m deep wells, 1. introduction.

• Strong environmental resilience
• Difficulty in signal recognition

2. Research on Mud Pulse Signal Recognition Algorithm

2.1. establishment of signal recognition model, 2.2. noise analysis, 2.3. signal processing algorithm, 2.3.1. discrete random signal filtering algorithm, 2.3.2. emd signal filtering algorithm.

• Extraction of extremum points

2.4. Signal Feature Extraction

2.5. classification of feature groups using grey relational analysis algorithm.

• Establishment of the pattern library matrix

3. Results Analysis and Discussion

3.1. recognition results of simulated signals, 3.2. recognition results of indoor experimental data, 4. conclusions, 5. future directions, author contributions, data availability statement, conflicts of interest, nomenclature.

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Share and Cite

Wang, Q.; Ji, G.; Guo, J.; Wu, K.; Mei, C.; Zeng, L.; Xue, Q. Research on Surface Processing Method of Pulse Transmission Signal of Amplitude-Modulated Drilling Fluid in 10,000-m Deep Wells. Electronics 2024 , 13 , 3231. https://doi.org/10.3390/electronics13163231

Wang Q, Ji G, Guo J, Wu K, Mei C, Zeng L, Xue Q. Research on Surface Processing Method of Pulse Transmission Signal of Amplitude-Modulated Drilling Fluid in 10,000-m Deep Wells. Electronics . 2024; 13(16):3231. https://doi.org/10.3390/electronics13163231

Wang, Qing, Guodong Ji, Jianhua Guo, Ke Wu, Chao Mei, Long Zeng, and Qilong Xue. 2024. "Research on Surface Processing Method of Pulse Transmission Signal of Amplitude-Modulated Drilling Fluid in 10,000-m Deep Wells" Electronics 13, no. 16: 3231. https://doi.org/10.3390/electronics13163231

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