Neuroscience in Psychiatry Training: How Much Do Residents Need To Know?
Bibliographic record
Abstract
OBJECTIVE: With the explosion of research in psychiatric neuroscience, the extent and means by which neuroscientific progress will translate into clinical care remains largely uncertain. The authors sought to determine how this dilemma is currently being played out in residency training programs, in which training directors must decide how best to integrate neuroscience teaching in a rapidly changing clinical landscape. METHOD: The authors surveyed U.S. and Canadian psychiatry residency training directors to characterize current and future trends in neuroscience education and to examine training directors' views on the relevance of neuroscience to clinical practice. RESULTS: The amount of neuroscience in residency curricula has increased significantly over the past 5 years, and further increases are expected in each specific neuroscience content area examined. While most training directors agreed that training in neuroscience was important for all residents, even those becoming primarily psychotherapists, relevance to future (but not current) practice was consistently cited as a motivating factor. CONCLUSIONS: While psychiatric residency programs continue to increase the neuroscience content of their curricula, it remains unclear how this added training will influence clinical work. Reframing current practices, including psychotherapy, into a neuroscientific context may ultimately prove more useful to trainees.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".