Identifying strategies that support equitable person-centred osteoarthritis care for diverse women: content analysis of guidelines
Bibliographic record
Abstract
Abstract Introduction Women are disproportionately impacted by osteoarthritis (OA) but less likely than men to access early diagnosis and management, or experience OA care tailored through person-centred approaches to their needs and preferences, particularly racialized women. One way to support clinicians in optimizing OA care is through clinical guidelines. We aimed to examine the content of OA guidelines for guidance on providing equitable, person-centred care to disadvantaged groups including women. Methods We searched indexed databases and websites for English-language OA-relevant guidelines published in 2000 or later by non-profit organizations. We used manifest content analysis to extract data, and summary statistics and text to describe guideline characteristics, person-centred care (PCC) using a six-domain PCC framework, OA prevalence or barriers by intersectional factors, and strategies to improve equitable access to OA care. Results We included 36 OA guidelines published from 2003 to 2021 in 8 regions or countries. Few (39%) development panels included patients. While most (81%) guidelines included at least one PCC domain, guidance was often brief or vague, few addressed exchange information, respond to emotions and manage uncertainty, and none referred to fostering a healing relationship. Few (39%) guidelines acknowledged or described greater prevalence of OA among particular groups; only 3 (8%) noted that socioeconomic status was a barrier to OA care, and only 2 (6%) offered guidance to clinicians on how to improve equitable access to OA care: assess acceptability, availability, accessibility, and affordability of self-management interventions; and employ risk assessment tools to identify patients without means to cope well at home after surgery. Conclusions This study revealed that OA guidelines do not support clinicians in caring for diverse persons with OA who face disadvantages due to intersectional factors that influence access to and quality of care. Developers could strengthen OA guidelines by incorporating guidance for PCC and for equity that could be drawn from existing frameworks and tools, and by including diverse persons with OA on guideline development panels. Future research is needed to identify multi-level (patient, clinician, system) strategies that could be implemented via guidelines or in other ways to improve equitable, person-centred OA care. Patient or public contribution This study was informed by a team of researchers, collaborators, and thirteen diverse women with lived experience, who contributed to planning, and data collection, analysis and interpretation by reviewing study materials and providing verbal (during meetings) and written (via email) feedback.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.159 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".