Promising Points for Intervention in Re-Imagining Partnered Research in Health Services Comment on "Experience of Health Leadership in Partnering with University-Based Researchers in Canada – A Call to ‘Re-imagine’ Research"
Bibliographic record
Abstract
In this commentary, we respond to Bowen and colleagues' empirical study of research partnerships between Canadian health organizations and university-based investigators. We draw on our experiences of university and health-services partnerships to elaborate on some of the misalignments between researchers and health services leaders identified by Bowen et al. We take up Bowen and colleagues' call to re-imagine research by proposing three promising points of intervention in research partnerships. These are: (1) orient towards research relationships rather than project-based partnerships; (2) recognize shared and diverging expectations and objectives; and (3) foster a more nuanced understanding of mutual gains.
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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 | 0.066 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.040 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.083 | 0.085 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".