Attitudes of Psychiatry Residents in Canadian Universities toward Neuroscience and Its Implication in Psychiatric Practice
Bibliographic record
Abstract
OBJECTIVE: Despite recent advances in neuroscience highlighting its potential applications in the assessment and treatment of psychiatric disorders, the training of psychiatrists in neuroscience is lacking. However, it is not clear to what extent Canadian trainees are interested in further learning and using neuroscience in their daily clinical practice. This study explored the attitudes of Canadian psychiatry trainees with regard to neuroscience education and training by asking them to assess their own understanding of neuroscience and the perceived relevance of neuroscience knowledge to effective psychiatric practice. METHODS: An online questionnaire was sent to psychiatry residents at Canadian universities. This questionnaire consisted of self-assessments of neuroscience knowledge, attitudes toward neuroscience education, preferences in learning modalities, and interest in specific neuroscience topics. RESULTS: One hundred and eleven psychiatry residents from psychiatry residency programs at Canadian universities responded to this survey. Participants represented trainees from all 5 years of residency. Almost half of all trainees (49.0%) reported their knowledge of neuroscience to be either "inadequate" or "less than adequate," and only 14.7% of trainees reported that they feel "comfortable" or "very comfortable" discussing neuroscience findings with their patients. 63.7% of Canadian trainees rated the quantity of neuroscience education in their residency program as either less than adequate or inadequate, and 46.1% rated the quality of their neuroscience education as "poor" or "very poor." The vast majority of participants (>70%) felt that additional neuroscience education would be moderately-to-hugely helpful in finding personalized treatments, discovering future treatments, destigmatizing patients with psychiatric illness, and understanding mental illness. CONCLUSIONS: Canadian trainees generally feel that their neuroscience knowledge and the neuroscience education they receive during their psychiatry residencies is inadequate. However, as the first step for any change, the majority of future Canadian psychiatrists are very motivated and have a positive attitude toward neuroscience learning.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".