Adaptation of a Shared Decision-Making Tool for Early Rheumatoid Arthritis Treatment Decisions with Indigenous Patients
Bibliographic record
Abstract
BACKGROUND: Patient decision aids (PtDAs) enable shared decision-making between patients and healthcare providers. Adaptations to PtDAs for use with populations facing inequities in healthcare can improve the relevancy of information presented, incorporate appropriate cultural context, and address health literacy concerns. Our objective was to adapt the Early RA (rheumatoid arthritis) PtDA for use with Canadian Indigenous patients. METHODS: The Early RA PtDA was modified through an iterative process using data obtained from semi-structured interviews of two sequential cohorts of Indigenous patients with RA. Interview data were analyzed using thematic analysis. RESULTS: Seven participants provided initial feedback on the existing PtDA. The modifications they suggested were made and shared with another nine participants to confirm acceptability and provide further feedback. The first cohort suggested revisions to clarify medical and cost coverage information, include Indigenous traditional healing practice options, simplify text, and include Indigenous images and colors aligned with Canadian Indigenous community representation. Additional revisions were suggested by the second cohort to increase the legibility of the text, insert more Indigenous imagery, address formulary coverage for non-status First Nations patients, and include information about lifestyle factors in managing RA. CONCLUSION: Incorporating Indigenous-specific adaptations in the design of PtDAs may increase use and relevancy to support engagement in treatment decisions, thereby supporting health-equity oriented health service interventions. Indigenous patient-specific evidence and translation of key words into the end-users' Indigenous languages should be included for implementation of the PtDA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.049 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".