Health Professions Education Scholarship Unit Leaders as Institutional Entrepreneurs
Bibliographic record
Abstract
PURPOSE: Health professions education scholarship units (HPESUs) are organizational structures within which a group is substantively engaged in health professions education scholarship. Little research investigates the strategies employed by HPESU administrative leaders to secure and maintain HPESU success. Using institutional entrepreneurship as a theoretical lens, this study asks: Do HPESU administrative leaders act as institutional entrepreneurs (IEs)? METHOD: This study recontextualizes two preexisting qualitative datasets that comprised interviews with leaders in health professions education in Canada (2011-2012) and Australia and New Zealand (2013-1014). Two researchers iteratively analyzed the data using the institutional entrepreneurship construct until consensus was achieved. A third investigator independently reviewed and contributed to the recontextualized analyses. A summary of the analyses was shared with all authors, and their feedback was incorporated into the final interpretations. RESULTS: HPESU leaders act as IEs in three ways. First, HPESU leaders construct arguments and position statements about how the HPESU resolves an institution's problem(s). This theorization discourse justifies the existence and support of the HPESU. Second, the leaders strategically cultivate relationships with the leader of the institution within which the HPESU sits, the leaders of large academic groups with which the HPESU partners, and the clinician educators who want careers in health professions education. Third, the leaders work to increase the local visibility of the HPESU. CONCLUSIONS: Practical insights into how institutional leaders interested in launching an HPESU can harness these findings are discussed.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".