An examination of the temporal and geographical patterns of psychiatric emergency service use by multiple visit patients as a means for their early detection
Bibliographic record
Abstract
BACKGROUND: Frequent users of the psychiatric emergency service (PES) place a heavy burden upon the mental health care delivery system. The aim of this study was to identify distinct temporal or geographical patterns of PES use by these patients as potential markers for their early detection. METHODS: Diagnostic profiles were obtained for patients making an intermediate (4 to 10) or a high (11 or more) number of visits to a general hospital PES in Montreal (Canada) between 1985 and 2004. Between-group comparisons were made with regards to several parameters. These included the time intervals between consecutive visits, visit clustering (single, repeating, and the time interval to the first cluster) and visits made to three other services where data was similarly acquired from 2002 to 2004. RESULTS: The two multiple visit groups differed with regards to diagnostic profiles and actual time between consecutive visits (significantly shorter in patients with 11 or more visits). Patients with 11 or more visits were more likely to have a single cluster (3 or more visits/3 months) or repeating clusters (4 visits/3 months) in their patterns of use. Personality disorders were more prevalent in patients with single clusters as they were, along with schizophrenia, in those with repeating clusters. In addition, clusters were found to occur sufficiently early so as to be potentially useful as markers for early detection. Ten percent of those with 11 or more visits and 16% of those with an intermediate number of visits frequented at least one other PES. A small number of patients, primarily those with substance abuse, made over 50% of their visits to other services. CONCLUSION: Temporal and geographical patterns of use differed significantly between the multiple visit groups. These patterns, combined with distinct diagnostic profiles, could potentially lead to the more rapid identification and treatment of specific sub-groups of multiple visit patients.
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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 | 0.000 | 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".