Factors influencing the scholarship and learning at McGill's faculty of medicine
Bibliographic record
Abstract
In recognition of the importance of the scholarship of teaching and learning (SoTL) in health professions education, faculties of medicine have taken a number of steps. These include acknowledging SoTL in promotion and tenure policies and implementing programs or workshops to encourage faculty to engage in educational research. Despite this, medical faculty members remain typically uninvolved in the SoTL. The aim of this study was to explore perceptions of McGill medical faculty about the SoTL and its value, and to identify perceived factors that enable and/or prevent them from engaging in the SoTL. A mixed- methods research design was used. A focus group and a web-based questionnaire were used as data sources. Study sample comprised medical faculty with known interest in medical education or who engaged in educational leadership. Ten participants attended the focus group; 54 completed surveys. Study results show that most respondents rate educational research equal in value to research in other areas. However, less than one third of respondents thought their institution rates it as highly as their clinical discipline research. Forty-one percent of respondents engaged in the SoTL. There was a positive significant correlation between academic rank and the number of SoTL publications. Main barriers identified in engaging in the SoTL were: lack of time, unfamiliarity with educational research methodology, and lack of funding. Factors perceived to promote engagement in SoTL were: career satisfaction, protected time, institutional acknowledgement, recognition in promotion and tenure decisions, educational research workshops, funding availability, faculty development workshops on teaching, mentors, awards, and supportive team members. Strategies to address the barriers identified, including a more extensive outreach policy to promote the SoTL, 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 | 0.013 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".