Priority strategies to reduce socio-gendered inequities in access to person-centred osteoarthritis care: Delphi survey
Bibliographic record
Abstract
OBJECTIVES: Osteoarthritis (OA) prevalence, severity and related comorbid conditions are greater among women compared with men, but women, particularly racialised women, are less likely than men to access OA care. We aimed to prioritise strategies needed to reduce inequities in OA management. DESIGN: Delphi survey of 28 strategies derived from primary research retained if at least 80% of respondents rated 6 or 7 on a 7-point Likert scale. SETTING: Online. PARTICIPANTS: 35 women of diverse ethno-cultural groups and 29 healthcare professionals of various specialties from across Canada. RESULTS: Of the 28 initial and 3 newly suggested strategies, 27 achieved consensus to retain: 20 in round 1 and 7 in round 2. Respondents retained 7 patient-level, 7 clinician-level and 13 system-level strategies. Women and professionals agreed on all but one patient-level strategy (eg, consider patients' cultural needs and economic circumstances) and all clinician-level strategies (eg, inquire about OA management needs and preferences). Some discrepancies emerged for system-level strategies that were more highly rated by women (eg, implement OA-specific clinics). Comments revealed general support among professionals for system-level strategies provided that additional funding or expanded scope of practice was targeted to only formally trained professionals and did not reduce funding for professionals who already managed OA. CONCLUSIONS: We identified multilevel strategies that could be implemented by healthcare professionals, organisations or systems to mitigate inequities and improve OA care for 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 | 0.059 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".