Teaching excellence, the hidden curriculum and complexity: an international comparative case study of two medical schools
Bibliographic record
Abstract
The perception held by clinical teachers of health professionals that their teaching efforts are under-valued by their education institutions persists despite intensive research and subsequent interventions to address this global problem. The purpose of this multi-site study is to examine how clinical teaching activities are organised, in order to reveal if there are underlying systemic factors that may contribute to this wicked problem. This study employed a cross-comparative case study design of one Singaporean and one Canadian medical school. Semi-structured interviews were conducted with organisational leaders (n = 23) who manage clinical teaching activities and/or have insights on their local assessment, support and recognition systems. Public records were also collected from each site (n = 24). A theory-driven content analysis using a complexity science interpretation of the concept of the Hidden Curriculum was conducted with both sets of data. The two sites were at different stages of maturation in respect of clinical teaching evaluation and feedback, support, and recognition and reward systems. Despite this, the interviews identified shared structure, process and culture-oriented challenges: low prioritisation of teaching, faculty demotivation and dissatisfaction across both research sites. Our findings suggest that a continued focus on structure and process-oriented reforms to elicit change is insufficient. Instead, further examination of site-specific, multiple intersecting academic and clinical cultures is needed. Future efforts to improve the value and drive the pursuit of teaching excellence will require multi-faceted structure, process and culture change approaches. We argue that Hafferty and Castellani's (The hidden curriculum: A theory of medical education, Routledge, 2009) re-conceptualisation of hidden curriculum through a complexity science lens should be used as a heuristic device to address future research and reform in local medical education contexts.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".