Can We Re-Imagine Research So It Is Timely, Relevant and Responsive?; Comment on “Experience of Health Leadership in Partnering with University-Based Researchers in Canada: A Call to ‘Re-Imagine’ Research”
Bibliographic record
Abstract
Partnerships between academic institutions and healthcare organisations have been proposed as an effective way to integrate academic research findings into changes in health policy and practice. Bowen and colleagues explore these partnerships from a different angle, analysing them in relation to the experiences of health system leaders. The authors made a call to re-imagine research, rethinking how we train applied health researchers, fund health research and evaluation and design studies and collaborations with the health sector. In this paper, I respond to this call by discussing three strategies we can use to make sure our research is timely, relevant and responsive to the needs and context of healthcare organisations: the widespread use of rapid research approaches, the integration of scoping stages in all studies, and the training of applied health researchers to work in the health system and develop collaborative relationships with staff.
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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 | 0.060 | 0.191 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.040 |
| Scholarly communication | 0.014 | 0.031 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.061 | 0.069 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".