Substance Misuse in the Psychiatric Emergency Service; A Descriptive Study
Bibliographic record
Abstract
Substance misuse is frequently encountered in the psychiatric emergency service (PES) and may take many forms, ranging from formal DSM-IV diagnoses to less obvious entities such as hazardous consumption. Detecting such patients using traditional screening instruments has proved problematic. We therefore undertook this study to more fully characterize substance misuse in the PES and to determine whether certain variables might help better screen these patients. We used a prospectively acquired database of over 18,000 visits made to four PESs during a 2-year period in the province of Quebec, Canada. One of the variables acquired was a subjective rating by the nursing staff as to whether substance misuse was a contributing factor to the visit (graded as direct, indirect, or not at all). Substance misuse accounted for 21% of all diagnoses and alcohol was the most frequent substance used. Patients were divided into those with primary (PSM), comorbid (CSM) or no substance misuse (NSM). Depressive disorders were the most frequent primary diagnoses in CSM, whereas personality and substance misuse disorders were frequent secondary diagnoses in PSM. Although many variables significantly differentiated the three groups, few were sufficiently detailed to be used as potential screening tools. Those situations that did have sufficient details included those with a previous history of substance misuse, substance misuse within 48 hours of the visit, and visits graded by the nursing staff as being directly and/or indirectly related to substance misuse. Variables related to substance misuse itself were the primary predictors of PSM and, less significantly, CSM. The nursing staff rating, although promising, was obtained in less than 30% of all visits, rendering its practical use difficult to assess.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".