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 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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".