Lessons learned from a health authority research capacity-building initiative
Bibliographic record
Abstract
Health systems worldwide are under pressure to deliver better care to more people with increasingly complex needs within constrained budgets. Research capacity building has been shown to help alleviate these challenges and is underway at hospitals and health authorities across the country; however, approaches vary widely and little exists in the Canadian literature to share experience and best practices. This article describes how a health authority in British Columbia, Canada, implemented and evaluated a 5-year research capacity-building program in partnership with a provincial health research funder. We offer lessons learned for those leading similar innovation-focused change management initiatives, including vision and buy in, complexity thinking, infrastructure, leadership, and coalition development. We suggest that collective learning and building a more robust research capacity-building literature can help health organizations and their partners take significant steps toward integrating research and care for a more effective, efficient, and patient-centred health system.
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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 | 0.143 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.015 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.008 | 0.014 |
| 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".