The sensemaking narratives of scientists working in health professions education scholarship units: The Canadian experience
Bibliographic record
Abstract
INTRODUCTION: To date, research studying health professions education scholarship units has overlooked the perspectives of research scientists in the field, despite their important role in these units. This research explores how health professions education scientists uphold and/or upend the institutional logics of the units they work within. METHODS: Recruited via snowball sampling, 29 Canadian health professions education scientists participated in semi-structured interviews that lasted between 32-55 min. Data analysis was informed by the theories of organizational institutionalism-specifically, the microfoundation element of sensemaking. RESULTS: Respondents' narrations of career success were overtly linked to their research-oriented pursuits above other expectations (i.e., teaching, service). DISCUSSION: Respondents' narrative revealed a mismatch between the value they associated with teaching- and service-related pursuits, and the value the institution associated with those pursuits. Participants indicated a need to reconceptualize the institutional value associated with these endeavors.
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.
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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.053 | 0.035 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| 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".