Improving healthcare consumer effectiveness: An An imated, S elf-serve, We b-based R esearch Tool (ANSWER) for people with early rheumatoid arthritis
Bibliographic record
Abstract
BACKGROUND: People with rheumatoid arthritis (RA) should use DMARDs (disease-modifying anti-rheumatic drugs) within the first three months of symptoms in order to prevent irreversible joint damage. However, recent studies report the delay in DMARD use ranges from 6.5 months to 11.5 months in Canada. While most health service delivery interventions are designed to improve the family physician's ability to refer to a rheumatologist and prescribe treatments, relatively little has been done to improve the delivery of credible, relevant, and user-friendly information for individuals to make treatment decisions. To address this care gap, the Animated, Self-serve, Web-based Research Tool (ANSWER) will be developed and evaluated to assist people in making decisions about the use of methotrexate, a type of DMARD. The objectives of this project are: 1) to develop ANSWER for people with early RA; and 2) to assess the extent to which ANSWER reduces people's decisional conflict about the use of methotrexate, improves their knowledge about RA, and improves their skills of being 'effective healthcare consumers'. METHODS/DESIGN: Consistent with the International Patient Decision Aid Standards, the development process of ANSWER will involve: 1.) creating a storyline and scripts based on the best evidence on the use of methotrexate and other management options in RA, and the contextual factors that affect a patient's decision to use a treatment as found in ERAHSE; 2.) using an interactive design methodology to create, test, analyze and refine the ANSWER prototype; 3.) testing the content and user interface with health professionals and patients; and 4.) conducting a pilot study with 51 patients, who are diagnosed with RA in the past 12 months, to assess the extent to which ANSWER improves the quality of their decisions, knowledge and skills in being effective consumers. DISCUSSION: We envision that the ANSWER will help accelerate the dissemination of knowledge and skills necessary for people with early RA to make informed choices about treatment and to manage their health. The latest in animation and online technology will ensure ANSWER fills a knowledge translation gap, focusing on the next generation of people living with RA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".