Multi-level strategies to improve equitable timely person-centred osteoarthritis care for diverse women: qualitative interviews with women and healthcare professionals
Bibliographic record
Abstract
BACKGROUND: Women are more likely to develop osteoarthritis (OA), and have greater OA pain and disability compared with men, but are less likely to receive guideline-recommended management, particularly racialized women. OA care of diverse women, and strategies to improve the quality of their OA care is understudied. The purpose of this study was to explore strategies to overcome barriers of access to OA care for diverse women. METHODS: We conducted qualitative interviews with key informants and used content analysis to identify themes regarding what constitutes person-centred OA care, barriers of OA care, and strategies to support equitable timely access to person-centred OA care. RESULTS: We interviewed 27 women who varied by ethno-cultural group (e.g. African or Caribbean Black, Chinese, Filipino, Indian, Pakistani, Caucasian), age, region of Canada, level of education, location of OA and years with OA; and 31 healthcare professionals who varied by profession (e.g. family physician, nurse practitioner, community pharmacist, physio- and occupational therapists, chiropractors, healthcare executives, policy-makers), career stage, region of Canada and type of organization. Participants within and across groups largely agreed on approaches for person-centred OA care across six domains: foster a healing relationship, exchange information, address emotions, manage uncertainty, share decisions and enable self-management. Participants identified 22 barriers of access and 18 strategies to overcome barriers at the patient- (e.g. educational sessions and materials that accommodate cultural norms offered in different languages and formats for persons affected by OA), healthcare professional- (e.g. medical and continuing education on OA and on providing OA care tailored to intersectional factors) and system- (e.g. public health campaigns to raise awareness of OA, and how to prevent and manage it; self-referral to and public funding for therapy, greater number and ethno-cultural diversity of healthcare professionals, healthcare policies that address the needs of diverse women, dedicated inter-professional OA clinics, and a national strategy to coordinate OA care) levels. CONCLUSIONS: This research contributes to a gap in knowledge of how to optimize OA care for disadvantaged groups including diverse women. Ongoing efforts are needed to examine how best to implement these strategies, which will require multi-sector collaboration and must engage diverse women.
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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 | 0.027 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".