Partnered health research in Canada: a cross-sectional survey of perceptions among researchers and knowledge users involved in funded projects between 2011 and 2019
Bibliographic record
Abstract
BACKGROUND: Engaging knowledge users in health research is accelerating in Canada. Our objective was to examine perceptions of partnered health research among individuals involved in funded Canadian partnered health research projects between 2011 and 2019. METHODS: We invited 2155 recipients of 1153 funded projects to answer a questionnaire probing project characteristics and perceptions of partnered health research. We described and compared perceived effects of involving knowledge users in the project, team cohesion, capability, opportunity and motivation for working in partnership between two categories of respondents: project role [nominated principal investigators (NPIs), other researchers and knowledge users] and gender. FINDINGS: We analysed data from 589 respondents (42% NPIs, 40% other researchers and 18% knowledge users; 56% women). Among the perceived effects variables, the proportion of ratings of significant influence of involving knowledge users in the project ranged between 12% and 63%. Cohesion, capability, opportunity and motivation variables ranged between 58% and 97% agreement. There were no significant differences between respondent groups for most variables. NPIs and women rated the overall influence of involving knowledge users as significant more than other respondent groups (p < 0.001). NPIs also reported higher agreement with feeling sufficiently included in team activities, pressure to engage and partnerships enabling personal goals (all p < 0.001). CONCLUSIONS: Most respondents held positive perceptions of working in partnership, although ratings of perceived effects indicated limited effects of involving knowledge users in specific research components and on project outcomes. Continued analysis of project outcomes may identify specific contexts and partnership characteristics associated with greater impact.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".