“It's Just Good Science”: A Qualitative Study Exploring Equity, Diversity, and Inclusion in Canadian Arthritis Research
Bibliographic record
Abstract
OBJECTIVE: Despite knowledge that health outcomes vary according to patient characteristics, identity, and geography, including underrepresented populations in arthritis research remains a challenge. We conducted interviews to explore how researchers in arthritis have used equity, diversity, and inclusion (EDI) principles to inform their research. METHODS: Semistructured interviews were conducted with individuals who 1) have experience conducting arthritis research studies, 2) reside in and/or conduct their research in Canada, and 3) speak English or French. Participants were recruited using purposive and respondent-driven sampling. Interviews were conducted over video call and audio recordings were transcribed. Template analysis was applied to interview transcripts to explore participant experiences and perceptions of EDI in arthritis research. RESULTS: Participants (n = 22) identified that a lack of representation in arthritis research translates to the inability to provide comprehensive care. Participants emphasized considering EDI early in all arthritis research to effectively affect a study. Themes were categorized as benefits, barriers, and facilitators. The perceived benefits were the ability to generate knowledge and reduce health disparities. Barriers included mistrust from historically exploited populations, unintended consequences, lack of access to research opportunities, and logistical challenges. Facilitators included building community partnerships, curating diverse research teams, incentivizing researchers and funder support, and fostering humility in research environments. CONCLUSION: Improving representation in research is needed to improve health outcomes for diverse groups of people living with arthritis. Identified barriers to EDI in research must be addressed and partnerships and supports must be facilitated to achieve more representation in arthritis research within 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 | 0.037 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.044 | 0.023 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".