What Can Health Services Researchers Offer Health Systems? Developing Meaningful Partnerships Between Academics and Health System Workers Comment on "Experience of Health Leadership in Partnering with University-Based Researchers in Canada - A Call to ‘Re-imagine’ Research"
Bibliographic record
Abstract
As healthcare researchers, we know very well our own experiences on the challenges of partnering with those in the health system to do collaborative, internationally-regarded studies aiming for impact. Bowen and colleagues' study in Canada empirically examines these issues from the other side, interviewing health system leaders about their perspectives of us researchers, research collaborations and the challenges and opportunities these pose. Based on their findings, they propose a need to re-imagine the contours of research. Inspired by that, in this commentary we examine the context for research partnerships and consider some of the emerging models for fostering more meaningful collaborations between researchers and those working in healthcare systems and organisations. Based on principles of embedded research and researchers, these models-including translational research networks (TRNs) and researcher-in-residence models-rely on a complex interplay of personal and interpersonal factors to be successful.
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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 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.079 | 0.062 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".