Obsessive-Compulsive Disorder: A Medical School Curriculum and Textbook Review
Bibliographic record
Abstract
OBJECTIVES: We conducted a curriculum review of Canadian undergraduate medical programs to identify why aggressive obsessions (among those with obsessive-compulsive disorder [OCD]) are so often misidentified by primary care physicians and professional students. METHODS: This study involved standardized interviews with representatives from Canadian medical schools regarding the content, time, and teaching styles used to deliver curricula related to OCD. Further, we utilized a set of standardized criteria to assess the OCD content of recommended textbooks from these schools. RESULTS: Canadian medical curricula failed to provide a comprehensive picture of OCD. One-third of medical programs did not provide an example of aggressive obsessions to students, with textbook case examples centered heavily (70%) on contamination or symmetry. Only 25% of programs (and 60% of textbooks) discussed the composition of the Unacceptable Thought Domain to include aggressive, sexual, and religious obsessions. Finally, over half of medical programs failed to indicate that aggressive obsessions are ego-dystonic and do not lead people to harm themselves or others. CONCLUSION: A series of recommendations are provided for medical schools intended to improve the comprehensiveness of OCD-related training.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".