Assessing the hidden curriculum in medical education: a scoping review and residency program’s reflection
Bibliographic record
Abstract
Background: While the hidden curriculum (HC) is becoming recognized as an important component of medical education, ideal methods of assessing the HC are not well known. The aim of this study was to review the literature for methods of assessing the HC in the context of healthcare education. Methods: We conducted a scoping review on methods to measure or assess the HC in accordance with the JBI Manual for Evidence Synthesis. Ovid MEDLINE, Ovid EMBASE, and ProQuest ERIC databases were searched from inception until August 2023. Studies which focused on healthcare education, including medicine, as well as other professions such as nursing, social work, pharmacy were included. We then obtained stakeholder feedback utilizing the results of this review to inform the ongoing HC assessment process within our own medical education program. Results: Of 141 studies included for full text review, 41 were included for analysis and data extraction. Most studies were conducted in North America and qualitative in nature. Physician education was best represented with most studies set in undergraduate medical education (n = 21, 51%). Assessment techniques included interviews (n = 19, 46%), cross-sectional surveys (n = 14, 34%), written reflections (n = 7, 17%), and direct observation of the working environment (n = 2, 5%). While attempts to create standardized HC evaluation methods were identified, there were no examples of implementation into an educational program formally or longitudinally. No studies reported on actions taken based on evaluation results. Confidential stakeholder feedback was obtained from postgraduate medical learners in our program, and this feedback was then used to modify our longitudinal HC assessment process. Conclusions: While the HC has as increasing presence in the medical education community, the ideal way to practically assess it within a healthcare education context remains unclear. We described the HC assessment process utilized at our program, which may be informative for other institutions attempting to implement a similar technique. Future attempts and studies would benefit from reporting longitudinal data and impacts of assessment results
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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 teacher head, 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".