Positioning the Work of Health Professions Education Scholarship Units: How Canadian Directors Harness Institutional Logics Within Institutional Orders to Convey Unit Legitimacy
Bibliographic record
Abstract
PURPOSE: Health professions education scholarship unit (HPESU) leaders often struggle to articulate their impact within local contexts. Previous research has described what markers of success and institutional logics to consider when crafting statements of impact; there is a need to clarify how HPESU leaders convey their messages to navigate competing demands. This study examined how leaders argue the legitimacy of their HPESUs' activities. METHOD: The institutional logics perspective offered a lens for understanding how legitimacy claims are constructed through larger institutional orders. Interviews with leaders from 12 Canadian HPESUs discussed their unit's work, the stakeholders that leaders sought to satisfy, and how they defined success. Data were generated in 2011-2012 and analyzed anew in 2017-2018. The authors inductively analyzed the data, using institutional logics and institutional orders as sensitizing concepts to identify the linguistic constructions harnessed by participants. RESULTS: HPESU leaders engaged with 2 dominant logics: research and service. These aligned with institutional orders: the profession and community, respectively. While a few HPESU leaders deployed only one logic throughout the course of an interview, many engaged with more than one, compartmentalizing logics specific to different audiences and activities or blending logics to create novel ways of framing their work. CONCLUSIONS: The institutional logics available in a context vary. What constitutes a compelling legitimacy claim is different from one institutional context to the next. The authors identify strategies that leaders used to position their HPESU for success and discuss the basis on which these claims are made.
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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 | 0.019 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.040 | 0.039 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".