Role Models and Teachers: medical students perception of teaching-learning methods in clinical settings, a qualitative study from Sri Lanka
Bibliographic record
Abstract
BACKGROUND: Medical education research in general, and those focusing on clinical settings in particular, have been a low priority in South Asia. This explorative study from 3 medical schools in Sri Lanka, a South Asian country, describes undergraduate medical students' experiences during their final year clinical training with the aim of understanding the teaching-learning experiences. METHODS: Using qualitative methods we conducted an exploratory study. Twenty eight graduates from 3 medical schools participated in individual interviews. Interview recordings were transcribed verbatim and analyzed using qualitative content analysis method. RESULTS: Emergent themes reveled 2 types of teaching-learning experiences, role modeling, and purposive teaching. In role modelling, students were expected to observe teachers while they conduct their clinical work, however, this method failed to create positive learning experiences. The clinical teachers who predominantly used this method appeared to be 'figurative' role models and were not perceived as modelling professional behaviors. In contrast, purposeful teaching allowed dedicated time for teacher-student interactions and teachers who created these learning experiences were more likely to be seen as 'true' role models. Students' responses and reciprocations to these interactions were influenced by their perception of teachers' behaviors, attitudes, and the type of teaching-learning situations created for them. CONCLUSIONS: Making a distinction between role modeling and purposeful teaching is important for students in clinical training settings. Clinical teachers' awareness of their own manifest professional characterizes, attitudes, and behaviors, could help create better teaching-learning experiences. Moreover, broader systemic reforms are needed to address the prevailing culture of teaching by humiliation and subordination.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".