Implementation of an innovative grant programme to build partnerships between researchers, decision-makers and practitioners: The experience of the Quebec Social Research Council
Bibliographic record
Abstract
OBJECTIVES: This paper examines a grant programme developed by the Quebec Social Research Council in the 1990s to encourage the building of research partnerships between researchers, decision-makers and practitioners. In particular, it studies the perceptions of key participants concerning the reasons behind the programme's successful implementation and growth. METHODS: In addition to secondary data about institutional involvement in the programme, 10 researchers and administrators were consulted as key informants. The method of concept mapping was used in order to draw out a consensus on the different factors associated with the successful implementation of the programme. RESULTS: The participants identified 10 main factors that help explain the programme's successful implementation. These factors were then grouped into a model containing four dimensions: the leadership and coherence shown in the programme's implementation; the presence of a favourable political and social conjuncture; the programme's responsiveness to the needs of health and social services institutions; and the programme's responsiveness to the needs of the university milieu. CONCLUSIONS: Although this model remains specific to the prevailing situation in Quebec at the time of its application, it may help stimulate reflection and contribute to an understanding of how research policies can encourage partnerships between researchers, practitioners and decision-makers.
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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 | 0.052 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".