Education scholarship: it's not just a question of ‘degree’
Bibliographic record
Abstract
BACKGROUND: Although medical faculty are frequently encouraged to participate in education scholarship, there is a paucity of literature addressing how to support those who wish to do so. AIMS: The purpose of this study was to explore faculty involvement in and support needs for pursuing education scholarship. METHODS: A purposive sample of 108 medical faculty with an interest in medical education were invited to participate in a two-phase, mixed-methods study (survey and focus groups). RESULTS: Seventy-three faculty (67.6%) completed the questionnaire with 16 subsequently participating in focus group sessions. Nearly 40% had enrolled in or completed formal education training. Although the majority had been involved in at least one education project during the past five years, few had received funding or published their work. Three support-related themes emerged: education research support; enhancing colleague interactions; and ongoing development activities. Three related barriers were identified: time, access to support staff, and knowledge of research methodology. No significant differences were identified between those with and without additional education training. CONCLUSIONS: Assisting faculty to participate in education scholarship is a complicated endeavor. Institutional supports should not be limited to those with advanced degrees nor rely on Master-level degree programs to provide all the necessary training.
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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 | 0.022 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".