The RANZCP Workforce Report: Action is needed, now
Bibliographic record
Abstract
OBJECTIVE: The RANZCP conducted an anonymous survey of 7200 members (trainees and psychiatrists) in December 2023, receiving 1269 responses, representing the views of roughly 1 in 6 members, and of the respondents, three quarters reported experiencing burnout in the last 3 years. We provide a commentary, citing evidence from relevant previous research, discussing the implications and proposing potential interventions. CONCLUSIONS: Members of the RANZCP reported worsening workforce shortages, with 9 in 10 respondents stating that these negatively impacted patient care, and 7 in 10 experiencing symptoms of burnout. Eighty per cent identified workforce shortages as the top contributing factor to such burnout. The aetiology of workforce shortages and burnout is likely due to operational and structural shortfalls in psychiatric services. However, public and private sector employment information was not included in the report. There are a range of strategic, evidence-based interventions to address the psychiatrist and trainee workforce challenges, comprising general healthcare service as well as specific initiatives. Based on the findings of the report, such interventions are needed, now.
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.
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 | 0.025 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.017 | 0.018 |
| 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".