Re-imagining Research: A Bold Call, but Bold Enough? Comment on "Experience of Health Leadership in Partnering with University-Based Researchers in Canada: A Call to ‘Re-Imagine’ Research"
Bibliographic record
Abstract
Many articles over the last two decades have enumerated barriers to and facilitators for evidence use in health systems. Bowen et al’s article "Response to Experience of Health Leadership in Partnering with University-Based Researchers: A Call to ‘Re-imagine Research’" furthers the debate by focusing on an under-explored research area (health system design and health service organization) with an under-studied stakeholder group (health system leaders), by undertaking a broad program of research on partnerships, and, based on participant responses, by calling for re-imagining of research itself. In response to the claim that the research community is not providing expertise to this pressing issue in the health system, I provide four high level reasons: partnerships mean different things to different people, our language does not reflect the reality we want, our health systems have yet to fully embrace evidence use, and complexity is easier to talk about than act within. Bowen et al’s study, and their broader program of research, is well-placed to explore these issues further, helping identify appropriate researcher-health system leader partnership models for various health system change projects. Given the positive shifts identified in this study, and the knowledge that participants demonstrate about what needs to change, the time is right for bold action, re-imagining not only research, but healthcare, such that the production and use of evidence for better health is embraced and supported.
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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 | 0.042 | 0.180 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.028 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.012 | 0.011 |
| Research integrity | 0.038 | 0.067 |
| Insufficient payload (model declined to judge) | 0.009 | 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".