Three things we learned along the way: lessons for training in psychiatric epidemiology
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic, and its associated mortality, morbidity, and deep social and economic impacts, was a global traumatic stressor that challenged population mental health and our de facto mental health care system in unprecedented ways. Yet, in many respects, this crisis is not new. Psychiatric epidemiologists have recognized for decades the need and unmet need of people in distress and the limits of the public mental health services in the United States. We argue that psychiatric epidemiologists have a critical role to play as we endeavor to address population mental health and draw attention to 3 areas of consideration: elevating population-based solutions; engaging equitably with lived experience; and interrogating recovery. Psychiatric epidemiology has a long history of both responding to and shaping our understanding of the relationships among psychiatric disorders and society through evolving methods and training, and the current sociohistorical moment again suggests that shifts in our practice can strengthen our field and its impact. This article is part of a Special Collection on Mental Health.
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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 | 0.075 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.016 | 0.034 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.017 | 0.047 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".