Partnership experiences: Involving decision-makers in the research process
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
OBJECTIVES: To describe researchers' experiences with involving health system managers and public policy-makers (i.e. decision-makers) in the research process, and decision-makers' experiences with the research process, including their assessments of the benefits and costs of the involvement, and their recommendations for facilitating it. METHODS: We conducted semi-structured interviews with principal investigators and research staff for the seven research programmes funded by the Canadian Health Services Research Foundation in the 1999 and 2000 competition years, and with the decision-makers they involved in the research programmes. RESULTS: We identify three models of decision-maker involvement--formal supporter, responsive audience, and integral partner--each of which yielded important contributions to the research process. Four factors--the stage of the research process, time commitment required, alignment between decision-maker expertise and programme needs, and an existing relationship between the researcher and decision-maker--influenced the role played by decision-makers. CONCLUSIONS: While on balance a beneficial experience, the further promotion of decision-maker involvement in the research process should involve helping researchers and decision-makers identify strategic opportunities for decision-maker involvement and support the costs associated with the involvement. Consideration should also be given to undertaking and evaluating interactions between researchers and decision-makers outside of the research process.
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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 | 0.082 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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