Partnership experiences: Involving decision-makers in the research process
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.127 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".