Widespread emergency department access block: a human rights issue in Australia?
Bibliographic record
Abstract
OBJECTIVE:: There are increasing demands on emergency psychiatrists with higher numbers of mental health presentations, and longer stays in emergency departments (EDs). Australia, like other English speaking countries, funds considerably lower numbers of psychiatric beds than average for Organisation for Economic Co-operation and Development (OECD) countries. Consequently, acute bed occupancy is high, and a bed is frequently unavailable when a person needs admission. Patients with serious mental illness can wait days in busy and overstimulating EDs, become agitated and assaultive, and then require chemical and physical restraint. All patients have a right to safe high quality care, but the paucity of beds deprives patients of this right. The Australasian College of Emergency Medicine recommends reporting ED access block to health ministers, and human rights and/or health rights commissioners, and recommends increased funding for inpatient psychiatric care, emergency mental health and after-hours community services, together with more alcohol and other drug programs. CONCLUSIONS:: It is challenging for emergency physicians and psychiatrists to provide optimal care for acutely unwell patients who stay extended periods in the ED. Increasing the availability of inpatient care must be considered as part of a comprehensive solution for minimising ED lengths of stay in Australia.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".