Is psychiatric emergency service (PES) use increasing over time?
Bibliographic record
Abstract
BACKGROUND: Several recent studies have reported a significant increase in medical emergency department (ED) use for reasons of mental health. The diagnostic profile of these patients however differs from that usually described for patients visiting the psychiatric emergency service (PES). Few studies have specifically focused upon long-term PES utilization rates. Those that do typically present data from the early 80s, suggesting that deinstitutionalization may be an important contributing factor to the increases found. The aim of this study was to assess PES use using a more recent time frame and, the effects of non-specific factors, such as population growth, on this use. METHODS: Visits per year at several different types of PESs were obtained; (a) for an 11-year period at a general hospital PES while the surrounding population remained stable, (b) at that same PES while the catchment area population doubled over a period of a few years, (c) for an 11-year period at two PESs without catchment areas while the surrounding population increased and (d-) for a 12-year period at a PES in a mental health facility while the surrounding population increased. Moderately conservative criteria were used to define either a trend or, a significant increase in utilization rates. RESULTS: Each site had an inherent, 7 to 15% yearly variability in the number of PES visits. Over time however, only those where the surrounding population increased (either by an increase in the catchment area size or a regional increase in the population census) showed a trend or, a significant increase in utilization rates. These increases however were modest and of the order of 12 to 19%. CONCLUSION: Long observation periods are required in order to detect stable changes in PES utilization rates over time. As such, population growth may be but one of several factors underlying these increases. Organizational changes in mental health care delivery in the vicinity of the services that showed an increase could also have contributed. These latter would simply have redistributed (to the PES) the pre existing pool of mental health care patients, resulting in an increase that is more apparent than real.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".