Medical residents’ and teachers’ perceptions of the digital format of nation-wide didactic courses for psychiatry residents in Sweden: a survey-based observational study
Bibliographic record
Abstract
BACKGROUND: This study aimed to explore residents' and teachers' perceptions of the digital format of Metis (a national education network in Sweden) didactic courses for psychiatry residents in Sweden to guide post-pandemic curriculum development. METHODS: An online attitude survey was developed and sent out to 725 residents in psychiatry and 237 course directors/teachers. Data were examined descriptively and group differences were analysed with independent sample t-tests. RESULTS: The survey was completed by 112 residents and 72 course directors/teachers. Perceptions of digital formats were quite similar between the two groups with some significant differences i.e., residents agreed more strongly than directors/teachers with the statement that Metis courses in digital format were of the same quality (or better) than the classroom-based format. Residents perceived the positive effects of using interactive tools more than directors/teachers. More than 40% of the responders in both groups preferred a return to classroom-based course meetings. Responders in both groups suggested that different forms of digital elements (e.g., video-based and sound-recorded lectures, digital-group discussions, virtual patients) could be incorporated into different phases in the courses. CONCLUSIONS: The study represents the current largest survey among residents in psychiatry and a teaching faculty in Sweden, to understand the impact of digitalization on the quality of residents' education during the pandemic. The results point towards applying a mixed format for training and education going forward, incorporating digital aspects into the national curriculum.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".