Evidence of commitment to research partnerships? Results of two web reviews
Bibliographic record
Abstract
BACKGROUND: Partnerships between academic researchers and health system leadership are often promoted by health research funding agencies as an important strategy in helping ensure that funded research is relevant and the results used. While potential benefits of such partnerships have been identified, there is limited guidance in the scientific literature for either healthcare organisations or researchers on how to select, build and manage effective research partnerships. Our main research objective was to explore the health system perspective on partnerships with researchers with a focus on issues related to the design and organisation of the health system and services. Two structured web reviews were conducted as one component of this larger study. METHODS: Two separate structured web reviews were conducted using structured data extraction tools. The first review focused on sites of health research bodies and those providing information on health system management and knowledge translation (n = 38) to identify what guidance to support partnerships might be available on websites commonly accessed by health leaders and researchers. The second reviewed sites from all health 'regions' in Canada (n = 64) to determine what criteria and standards were currently used in guiding decisions to engage in research partnerships; phone follow-up ensured all relevant information was collected. RESULTS: Absence of guidance on partnerships between research institutions and health system leaders was found. In the first review, absence of guidance on research partnerships and knowledge coproduction was striking and in contrast with coverage of other forms of collaboration such as patient/community engagement. In the second review, little evidence of criteria and standards regarding research partnerships was found. Difficulties in finding appropriate contact information for those responsible for research and obtaining a response were commonly experienced. CONCLUSION: Guidance related to health system partnerships with academic researchers is lacking on websites that should promote and support such collaborations. Health region websites provide little evidence of partnership criteria and often do not make contact information to research leaders within health systems readily available; this may hinder partnership development between health systems and academia.
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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 | 0.114 | 0.464 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.052 | 0.045 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".