Researcher-decision-maker partnerships in health services research: Practical challenges, guiding principles
Bibliographic record
Abstract
BACKGROUND: In health services research, there is a growing view that partnerships between researchers and decision-makers (i.e., collaborative research teams) will enhance the effective translation and use of research results into policy and practice. For this reason, there is an increasing expectation by health research funding agencies that health system managers, policy-makers, practitioners and clinicians will be members of funded research teams. While this view has merit to improve the uptake of research findings, the practical challenges of building and sustaining collaborative research teams with members from both inside and outside the research setting requires consideration. A small body of literature has discussed issues that may arise when conducting research in one's own setting; however, there is a lack of clear guidance to deal with practical challenges that may arise in research teams that include team members who have links with the organization/community being studied (i.e., are "insiders"). DISCUSSION: In this article, we discuss a researcher-decision-maker partnership that investigated practice in primary care networks in Alberta. Specifically, we report on processes to guide the role clarification of insider team members where research activities may pose potential risk to participants or the team members (e.g., access to raw data). SUMMARY: These guiding principles could provide a useful discussion point for researchers and decision-makers engaged in health services research.
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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 | 0.564 | 0.287 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.031 | 0.151 |
| Scholarly communication | 0.043 | 0.033 |
| Open science | 0.016 | 0.038 |
| Research integrity | 0.044 | 0.045 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".