Recruitment and retention of tutors in problem-based learning: why teachers in medical education tutor
Bibliographic record
Abstract
INTRODUCTION: Problem-based learning (PBL) is resource-intensive, particularly as it relates to tutors for small group learning. This study explores the factors that contributed to tutor participation in PBL in a medical training program, examining tutor recruitment and retention within the larger scope of teacher satisfaction and motivation in higher education. METHOD: From 2007 to 2010, following the introduction of new PBL-based curriculum in undergraduate medical education, all faculty members serving as tutors were invited to attend an interview as part of this study. Semi-structured interviews approximately one hour in length were conducted with 14 individuals- 11 who had tutored in PBL within the Faculty of Medicine and Dentistry and 3 faculty members who had chosen not to participate in PBL. Thematic analysis was employed as the framework for analysis of the data. RESULTS: Seven factors were identified as affecting recruitment and retention of tutors in the undergraduate medical education program. DISCUSSION: We suggest that identification and strengthening of the factors that promote tutor recruitment and retention may serve to strengthen PBL initiatives and, furthermore, may increase our understanding of motivation by academics in other aspects of medical education.
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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 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".