Evolving Roles and Needs of Psychiatry Chief Residents During the COVID-19 Pandemic and Beyond
Bibliographic record
Abstract
Psychiatry chief residents have diverse leadership roles within psychiatry residency programs. Chief residents have historically been viewed as "middle managers", and other leadership roles include administrative work, teaching, and advocacy for residents. Chief residents also help in managing the logistics of complex healthcare systems and mediating between many groups with conflicting needs and perspectives. The COVID-19 pandemic has changed the functioning of psychiatry residency programs, and this has also led to the evolution of the roles of the chief residents in psychiatry. During the COVID-19 pandemic, the chief residents had to help with adapting to the changes in teaching and clinical work with residents and faculty. They had to liaison with various healthcare providers in making decisions related to COVID-19 in residency programs. Along with these changes, chief residents also had to advocate for the wellbeing and needs of their fellow residents. This perspective article is written by authors who have served during or after the transition to the COVID-19 pandemic. We discuss our experiences as chief residents as well as evolving roles and wellness needs of chief residents in psychiatry. Based on the administrative, advocacy, academic and middle management roles of chief residents in psychiatry and their wellbeing, we also make recommendations for support and interventions needed for chief residents in the context of the COVID-19 pandemic and beyond.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".