Building research culture and capacity in academic family medicine departments: Insights from a simulation workshop.
Bibliographic record
Abstract
OBJECTIVE: To use data from a workshop in which various representatives from departments of family medicine (DFMs) aimed to identify strategies to increase research activity, particularly among clinical faculty members. DESIGN: Descriptive qualitative study using data from a workshop in which participants role-played (ie, as clinician-teachers, department chairs, and mentors) and, while in the role-playing scenario, were asked to imagine strategies that would encourage the clinical faculty members to engage in research. SETTING: The 2014 North American Primary Care Research Group Annual Meeting in New York City, NY. PARTICIPANTS: Thirty-two workshop participants who belonged to DFMs and other academic primary care organizations: 18 from Canada, 11 from the United States, 2 from Australia, and 1 from the Netherlands. METHODS: Facilitators recorded the strategies at the workshop. Strategies were organized into themes and vetted by facilitators to ensure that they adequately represented the data. Finalized themes were compared and integrated across scenarios. MAIN FINDINGS: Participants enthusiastically and productively engaged in the role-playing scenarios. The themes that emerged from the workshop discussions indicated that in order to increase clinician-teacher engagement in research, the following factors needed to be attended to: gaining confidence in conducting research; finding research topics that have personal relevance; presenting clarity of expectations; fostering collaborative relationships; using a tailored approach; providing resources, structures, and processes; and having leadership and vision. Finally, it was important to recognize these efforts in the context of the existing research environment of the DFM and the various responsibilities of clinician-teachers. CONCLUSION: The analysis of data arising from this simulation workshop elucidated practical strategies for building and sustaining research in DFMs. There is a clear indication that one size does not fit all with respect to strategies for building a research culture in a DFM; the authors' recommendations guide departments to tailor strategies to their unique context.
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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 | 0.060 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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, 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".