Living on Site While Renovating; Flexible Instructional Design of Post-Graduate Medical Training
Bibliographic record
Abstract
Background: Developing theoretical courses for post-graduate medical training that are aligned to current workplace-based learning practices and adaptive to change in the field is challenging, especially in (sub) specialties where time for re-design is limited and needs to be performed while education continues. Approach: An instructional design method was applied based on flexible co-design to improve post-graduate theoretical courses in child and adolescent psychiatry (CAP) in the Netherlands. In four phases over a period of three years, courses were re-designed at a national level. Evaluation: Once common vision and learning goals were agreed upon and the prototype was developed (phases 1 and 2), the first courses could be tested in daily practice (phase 3). Phase 4 refined these courses in brief iterative cycles and allowed for designing additional courses building on and adding to previous experiences in brief iterative cycles. The resulting national theoretical courses re-allocated resources previously spent on a local level using easily accessible online tools. This allowed trainees to align content with their clinical rotations, personal preferences and training schedules. Reflection: The development of theoretical courses for post-graduate medical training in smaller medical (sub-)specialties with limited resources may profit from a flexible instructional design method. We consider the potential merit of such a method to other medical specialties and other (inter-)national efforts to develop theoretical teaching courses. A longer-term implementation evaluation is needed to show to what extent the investment made in the re-design proves to be future-proof and enables rapid adaptation to changes in the field.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".