How has private psychiatry in Australia responded to the COVID-19 pandemic?
Bibliographic record
Abstract
The COVID-19 pandemic has highlighted the role of private psychiatry, which responded to the crisis with expanded outpatient/telehealth practice and ongoing private hospital care, complementary to public sector care. Despite the significance of the private sector, it has been largely overlooked in recent reports from the Productivity Commission, the Royal Commission into Victorian Mental Health Services and the Fifth National Mental Health Plan. We highlight research findings that demonstrate the scope and resilience of the private sector in meeting the challenge of the pandemic, as well as previous private–public sector collaborative initiatives that could be revivified.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.021 | 0.028 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".