Advancing equity diversity and inclusion considerations and application in patient-oriented research in British Columbia
Bibliographic record
Abstract
Patient-oriented research (POR) engages people with lived experience as partners in health research priority-setting, design, and knowledge translation, ensuring research outcomes are relevant for people accessing healthcare. POR is supported by the Canadian Institutes of Health Research (CIHR)-led Strategy for Patient Oriented Research (SPOR) nationally, and the British Columbia Support for People and Patient-Oriented Research and Trials (BC SUPPORT) Unit provincially. Equity, diversity, and inclusion (EDI) are foundational to effective and impactful POR. We outline the BC SUPPORT Unit's journey to integrate EDI considerations in POR over two phases and share lessons learned. Initial work focused on advancing methods to understand engagement barriers faced by those underrepresented in POR. Subsequent efforts saw integration of EDI considerations and practices across the BC SUPPORT Unit's activities. A baseline EDI assessment identified knowledge gaps in actioning EDI and navigating the fear of making mistakes, informing the creation of a workshop series for ongoing EDI training. We also describe how an integrated knowledge translation platform, the Tapestry Tool, facilitated co-creation and sharing of resources throughout this work. We share how purposeful and inclusive community engagement, combined with methodological approaches to embed EDI in POR, can promote lasting change within research ecosystems and beyond.
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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 | 0.138 | 0.137 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.029 | 0.020 |
| Scholarly communication | 0.022 | 0.006 |
| Open science | 0.004 | 0.035 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".