Research funder required research partnerships: a qualitative inquiry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.090 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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 it