Building Research Capacity for Impact in Applied Health Services Research Partnerships Comment on "Experience of Health Leadership in Partnering With University-Based Researchers in Canada – A Call to "Re-imagine" Research"
Bibliographic record
Abstract
Bowen and colleagues ask us to re-imagine how to conduct research in academic-practice partnerships, and to develop capacity in the applied research and health workforce to do this. This commentary reinforces their messages, and describes a framework of research capacity development for impact (RCDi) which emphasizes active and continuous experiential learning within research partnerships. The RCDi framework includes the need to focus on multiple levels in the collaboration architecture, and describes principles of working that aims to increase impact on services, and learning opportunities for all partners.
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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 | 0.020 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.012 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.068 | 0.060 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".