Patients languishing in emergency departments: A descriptive analysis of mental health-related emergency department presentations in Australia between 2016-17 and 2020-21
Bibliographic record
Abstract
Objective In the context of concerns regarding hospital access block, this paper provides a descriptive longitudinal analysis of mental health–related ED episodes in Australian public hospitals between 2016-17 and 2020-21. Method We descriptively analysed Australian Institute of Health and Welfare data for mental health–related ED presentations, outcomes and 5-year trends for Australian public hospitals. Results There were more than 300,000 Australian mental health–related ED presentations in 2020-21. Presentations increased by an average annual rate of 2.8% between 2016-17 and 2020-21, commonly involving first responder (police, paramedic) attendance. From 2016-17 to 2020-21, the average annual rate of mental health–related ED presentations receiving a triage category of resuscitation increased by 13.7%, emergency by 9.4% and urgent by 4.7%. 90% of MH-related ED presentations were completed within 14 h, which was longer than the 90 th percentile for all ED presentations (up to 8 h). Conclusions Current mental health policies have not stemmed the rising tide of ED presentations. Mental health–related ED presentations are increasing in number and severity, likely due to health systemic and societal factors. Psychiatry patients stay longer in EDs than other patients. Healthcare reforms should be targeted to provide the best outcome based on principles of equity of access.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".