Collaborative health research partnerships: a survey of researcher and knowledge-user attitudes and perceptions
Bibliographic record
Abstract
Abstract Background Integrated knowledge translation describes the process of partnered research between different stakeholders with the goal of producing research that ultimately achieves a greater impact when put into practice. A better understanding of research partnerships and integrated knowledge translation has implications for future partnerships and collaborative initiatives in practice. Our research describes and expands upon previous work done to identify barriers and attitudes toward collaboration in the context of research funding opportunities that required researcher–knowledge-user partnerships. Methods A survey was sent out to researchers funded by the Canadian Institutes of Health Research and knowledge-users who worked collaboratively on their research projects. There were two mirror versions of the survey, one for researchers and one for knowledge-users. Descriptive statistics, χ2 analysis and Mann–Whitney U analysis were used to understand the processes, barriers, perceived impact and sustainability of the partnerships. Results The results revealed that, although there were differences in the roles of researchers and knowledge-users, both groups felt very positive towards their partnerships. Some of the barriers identified as inhibiting effective partnerships were resource constraints (funding/time) and differences in contribution and involvement amongst team members. Despite these barriers, both researchers and knowledge-users felt that the partnership was not only sustainable, but also helped create an impact. Conclusions Our results provide useful information for funding agencies launching opportunities requiring or encouraging collaborative research projects between researchers and knowledge-users.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.031 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".