DECISION MAKERS’ EXPERIENCES OF COLLABORATING WITH RESEARCH TEAMS ON FEDERALLY FUNDED HEALTH RESEARCH INITIATIVES: AN INTERPRETIVE DESCRIPTIVE QUALITATIVE STUDY
Bibliographic record
Abstract
Consistent with the paradigm of evidence informed decision making we assume that research findings are integrated into health services practice and policy. However, there is a gap betweeen research findings and usual practice. Collaborative research, where researchers are encouraged to partner with decision makers to conduct mutually agreed and relevant research, may facilitate prompt utilization of new findings. My study explored decision makers’ experiences of collaborative teams executing federally funded health research. The principles of interpretive description were used to guide sampling, data collection, and analysis. A purposeful sample of 27 decision makers, collaborating on Partnerships for Health System Improvement (PHSI) projects funded by the Canadian Institutes of Health Research, participated in two in-depth interviews. Conventional content analysis was used to identify concepts. The conceptual framework was developed inductively from the descriptive data and provided a structure for interpreting decision maker perspectives. The framework posits an explanation leading to contextual understanding of their experiences. This study describes factors affecting PHSI engagement that include: availability of new funding; positive history with the researcher; prospect of tangible benefits to constituents of decision makers; desire to contribute to research that informs health services programs and policies; capacity building; and knowledge creation. The partnership process is facilitated by fostering connections; identifying required skills and competencies; maintaining a sustainable focus of inquiry; clarifying roles and responsibilities; cultivating a nurturing learning environment. My findings will inform decision makers, researchers, and funding agencies about the experience and legacy of collaborative research partnerships.
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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 | 0.066 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.026 | 0.030 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.005 | 0.008 |
| 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".