Growing the Psychiatry Workforce Through Expansion or Creation of Residencies and Fellowships: the Results of a Survey by the AADPRT Workforce Task Force
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: The USA needs to produce more psychiatrists to meet projected workforce deficits. The American Association of Directors of Psychiatric Residency Training Directors (AADPRT) sought to examine opportunities for and obstacles to expanding or creating residencies and fellowships. METHODS: In November 2019, the authors conducted a survey of residency and fellowship directors. The survey gathered information about new positions, new programs, participation in interprofessional education, and loss of residency or fellowship positions. RESULTS: The survey was distributed to psychiatry residency (N=231) and fellowship (N=194) directors, with a response rate of 33.4%. One quarter of responding residencies and fellowships reported creating new programs; 24.7% of residency and 17.5% of fellowships reported expansion. The most common reason to develop or expand programs was the shortage of psychiatrists, with the local institution as the most common funding source. Fifty-seven percent reported that they had wanted to expand, but faced barriers, primarily lack of funding. Recruitment and retention of faculty are major challenges. Psychiatry departments frequently (87.5%) participate in interprofessional education, generally perceived as positive. Unfortunately, 15.7% of respondents reported loss of positions or closure of programs. CONCLUSIONS: Creating and expanding residencies and fellowships are common strategies for addressing the shortage of psychiatrists. Barriers include lack of funding and challenges recruiting/retaining faculty. The loss of residency/fellowship positions or closure of programs is a worrisome trend.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it