The hidden curriculum across medical disciplines: an examination of scope, impact, and context
Bibliographic record
Abstract
Background: While research suggests that manifestations of the hidden curriculum (HC) phenomenon have the potential to reinforce or undermine the values of an institution, very few studies have comprehensively measured its scope, impact, and the varied clinical teaching and learning contexts within which they occur. We explored the HC and examined the validity of newly developed constructs and determined the influence of context on the HC. Methods: We surveyed medical students (n =182), residents (n =148), and faculty (n = 140) from all disciplines at our institution between 2019 and 2020. Based on prior research and expertise, we measured participants’ experience with the HC including perceptions of respect and disrespect for different medical disciplines, settings in which the HC is experienced, impact of the HC, personal actions, efficacy, and their institutional perceptions. We examined the factor structure, reliability, and validity of the HC constructs using exploratory factor analysis Cronbach’s alpha, regression analysis and Pearson’s correlations. Results: Expert judges (physician faculty and medical learners) confirmed the content validity of the items used and the analysis revealed new HC constructs reflecting negative expressions, positive impacts and expressions, negative impacts, personal actions, and positive institutional perceptions of the HC. Evidence for criterion validity was found for the negative impacts and the personal actions constructs and were significantly associated with the stage of respondents’ career and gender. Support for convergent validity was obtained for HC constructs that were significantly correlated with certain contexts within which the HC occurs. Conclusion: More unique dimensions and contexts of the HC exist than have been previously documented. The findings demonstrate that specific clinical contexts can be targeted to improve negative expressions and impacts of the HC.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| 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".