Qualitative Study of Treatment Preferences for Rheumatoid Arthritis and Pharmacotherapy Acceptance: Indigenous Patient Perspectives
Bibliographic record
Abstract
OBJECTIVE: To explore patient preferences that influence decision-making in the management of rheumatoid arthritis (RA) by indigenous patients living in southern Alberta, Canada. METHODS: We conducted a qualitative narrative-based study within a social constructivist framework. Thirteen in-depth interviews with indigenous patients with RA who had attended 1 of 3 rheumatology practices in southern Alberta (1 rural and 2 urban) were completed. Codes generated through 2 phases of analysis were condensed into main themes, triangulated, and used to produce theoretical statements. RESULTS: Patients preferred to use a combination of nonpharmacologic and pharmacologic treatments to manage their RA. Nonpharmacologic treatments included physical, mental, emotional, and spiritual strategies. Patients' preferences for taking medications varied and were influenced by factors that were clinical (i.e., trust in health providers and understanding drugs' mechanisms of action, benefits, harms, and administration burden), familial (i.e., support), and societal (i.e., access to medications and stigmatization of drug dependency). CONCLUSION: Indigenous patients apply a holistic approach to the nonpharmacologic management of RA. Increases in preferences for RA medications could be supported through enhanced communication strategies to increase patient understanding of medication effects and health provider recognition of societal and familial influences on patient decisions. A patient-provider relationship based on trust was fundamental to reaching mutual understanding and should be fostered by models of practice that promote cultural safety, empathy, compassion, openness, acknowledgment, and respect of cultural differences.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".