Research funder required research partnerships: a qualitative inquiry
Bibliographic record
Abstract
BACKGROUND: Researchers and funding agencies are increasingly showing interest in the application of research findings and focusing attention on engagement of knowledge-users in the research process as a means of increasing the uptake of research findings. The expectation is that research findings derived from these researcher-knowledge-user partnerships will be more readily applied when they became available. The objective of this study was to investigate the experiences, perceived barriers, successes, and opinions of researchers and knowledge-users funded under the Canadian Institutes of Health Research's integrated Knowledge Translation funding opportunities for a better understanding of these collaborations. METHODS: Participants, both researchers and knowledge-users, completed an online survey followed by an individual semi-structured phone interview supporting a mixed methods study. The interviews were analyzed qualitatively using a modified grounded theory approach. RESULTS: Survey analysis identified three major partnership types: token, asymmetric, and egalitarian. Interview analysis revealed trends in perceived barriers and successes directly related to the partnership formation and style. While all partnerships experienced barriers, token partnerships had the most challenges and general poor perception of partnerships. The majority of respondents found that common goals and equality in partnerships did not remove barriers but increased participants' ability to look for solutions. CONCLUSIONS: We learned of effective mechanisms and strategies used by researchers and knowledge-users for mitigating barriers when collaborating. Funders could take a larger role in helping facilitate, nurture, and sustain the partnerships to which they award grants.
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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 | 0.050 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| 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".