Researcher-decision-maker partnerships in health services research: Practical challenges, guiding principles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.264 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it