Psychotic‐like experiences and associated factors in resident physicians: A <scp>Canadian</scp> cross‐sectional study
Bibliographic record
Abstract
AIM: Medical residency training is associated with a range of sociodemographic, lifestyle and mental health factors that may confer higher risk for psychotic-like experiences (PLEs) in residents, yet little research has examined this question. Thus, we aimed to document the prevalence and associated factors of PLEs among resident physicians. METHODS: Physicians enrolled in residency programmes in the Province of Québec, Canada (four universities) were recruited in Fall 2022 via their programme coordinators and social media. They completed an online questionnaire assessing PLEs in the past 3 months (the 15-item Community Assessment of Psychic Experiences), as well as sociodemographic characteristics, lifestyle and mental health. Analyses included survey weights and gamma regressions. RESULTS: The sample included 502 residents (mean age, 27.6 years; 65.9% women). Only 1.3% (95% CI: 0.5%, 4.0%) of residents met the screening cut-off for psychotic disorder. Factors associated with higher scores for PLEs included racialised minority status (relative difference: +7.5%; 95% CI: +2.2%, +13.2%) and English versus French as preferred language (relative difference: +7.9% 95% CI: +3.1%, +12.9%), as well as each additional point on scales of depression (relative difference: +0.8%; 95% CI: +0.3%, +1.3%) and anxiety (relative difference: +1.3%; 95% CI: +0.8%, +1.7%). In secondary analyses, racialised minority status was associated with persecutory items, but not with other PLEs. Gender, residency programmes and lifestyle variables were not associated with PLEs. CONCLUSIONS: This study found low reports of PLEs in a sample of resident physicians. Associations of PLEs with minoritised status may reflect experiences of discrimination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".