Decision Needs and Preferred Strategies for Shared <scp>Decision‐Making</scp> in Rheumatoid Arthritis: Perspectives of Canadian Urban Indigenous Women
Bibliographic record
Abstract
OBJECTIVE: Decision-making for treatment of rheumatoid arthritis (RA) is complex, with multiple beneficial medication options available, but with the potential for treatment-related adverse effects and significant economic considerations. Indigenous patients make treatment decisions informed by an interplay of clinical, family, and societal factors. Shared decision-making may represent an approach to support treatment decisions in a culturally congruent manner. Our objective was to identify aspects of arthritis care that Indigenous participants found relevant for shared decision-making and to explore preferences for shared decision-making strategies. METHODS: A purposive sampling from rheumatology clinics that provide services to Indigenous patients in a Canadian urban center was used to recruit participants for interviews. Seven participants were recruited to reach content saturation. Interview content was coded by 2 individuals, including an Indigenous patient with RA, and the data were analyzed via thematic analysis. RESULTS: Participants were all women ages 37-61 years living with RA. Participants supported the idea that shared decision-making would be beneficial, primarily to support decisions around treatment plans and medication changes. Shared decision-making approaches would need to reflect Indigenous-specific content areas, such as benefits and risks of therapy informed by data from Indigenous patient populations and inclusion of traditional modes of healing. All participants were interested in having a decision coach and preferred that decision aids be in both paper and electronic formats for accessibility. CONCLUSION: This study advances knowledge in the priority areas and specific content needed in the shared decision-making process and the preferences of shared decision-making strategies relevant and appropriate for urban Indigenous women living with RA in Canada.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.014 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".