Beyond 'Two Cultures': Guidance for Establishing Effective Researcher/Health System Partnerships
Bibliographic record
Abstract
Background The current literature proposing criteria and guidelines for collaborative health system research often fails to differentiate between: (a) various types of partnerships, (b) collaborations formed for the specific purpose of developing a research proposal and those based on long-standing relationships, (c) researcher vs. decision-maker initiatives, and (d) the underlying drivers for the collaboration. Methods Qualitative interviews were conducted with 16 decision-makers and researchers who partnered on a Canadian major peer-reviewed grant proposal in 2013. Objectives of this exploration of participants’ experiences with health system research collaboration were to: (a) explore perspectives and experience with research collaboration in general; (b) identify characteristics and strategies associated with effective partnerships; and (c) provide guidance for development of effective research partnerships. Interviews were audio-recorded and transcribed: transcripts were qualitatively analyzed using a general inductive approach. Results Findings suggest that the common “two cultures” approach to research/decision-maker collaboration provides an inadequate framework for understanding the complexity of research partnerships. Many commonlyidentified challenges to researcher/knowledge user (KU) collaboration are experienced as manageable by experienced research teams. Additional challenges (past experience with research and researchers; issues arising from previous collaboration; and health system dynamics) may be experienced in partnerships based on existing collaborations, and interact with partnership demands of time and communication. Current research practice may discourage KUs from engaging in collaborative research, in spite of strong beliefs in its potential benefits. Practical suggestions for supporting collaborations designed to respond to real-time health system challenges were identified. Conclusion Participants’ experience with previous research activities, factors related to the established collaboration, and interpersonal, intra- and inter-organizational dynamics may present additional challenges to research partnerships built on existing collaboration. Differences between researchers and KUs may pose no greater challenges than differences among KUs (at various levels, and representing diverse perspectives and organizations) themselves. Effective “relationship brokering” is essential for meaningful collaboration.
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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 | 0.361 | 0.378 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.042 | 0.060 |
| Scholarly communication | 0.044 | 0.055 |
| Open science | 0.013 | 0.048 |
| Research integrity | 0.023 | 0.024 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".