Professional and student understanding of harm obsessive–compulsive disorder: A vignette study.
Bibliographic record
Abstract
Obsessive compulsive-disorder (OCD) is characterized by obsessions and compulsions that differ significantly across patients. Lesser-known, Harm-related obsessions (i.e., fears of harming others or oneself; Harm OCD) can present in varying ways and are often misidentified – even by professionals – compared to more “prototypical” Contamination obsessions. However, research had not yet tested a vignette design specific to differing presentations of Harm OCD across a sample of professionals and students, particularly medical students. This study surveyed a sample of professionals (registered psychologists, general practitioners; n = 73), doctoral psychology students (n = 92), and medical students (n = 143), gathering diagnostic impressions and risk judgements for one of several Harm OCD vignettes (i.e., fears of harming one’s infant, of smothering one’s partner, of blurting an insult, or of completing suicide) or a social anxiety (control) vignette as compared to a Contamination OCD vignette. Harm OCD was significantly less likely to be identified (76%) than Contamination OCD (97%) through open-ended identification, and social anxiety when using ranked identification methods. Further, professionals and doctoral psychology students were significantly better able to identify Harm OCD than M.D. students, and characters with Harm OCD were perceived as more likely to harm others compared to those with Contamination OCD . The current findings support the need for accurate media representation of the varying OCD presentations, as well as improvement in OCD medical education.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".