Patients assessed by the liaison psychiatric team in the emergency department of a regional hospital in Canada – general characteristics and gender differences
Bibliographic record
Abstract
OBJECTIVE: To describe the characteristics of liaison psychiatric patients in the emergency department (ED) of a medium sized hospital in the oil sands region of Northern Alberta. METHODS: ED psychiatry services users were evaluated using a data assessment tool designed to capture all relevant demographic and clinical characteristics. RESULTS: Overall, 477 patients (48.2% male) were assessed by the psychiatric team over the 12 month period. There was a fairly balanced distribution by age, ethnicity and relationship status between both sexes. The majority of patients with a history of self-harm or childhood sexual abuse were female while male patients were significantly more likely to report medication non-compliance. A higher proportion of female patients had depressive disorders and personality disorders while a higher proportion of male patients had anxiety disorders, bipolar and related disorders, schizophrenia spectrum disorders and substance-related disorders. Nearly one in five patients was admitted for inpatient treatment with a significantly higher proportion of males being admitted involuntarily. CONCLUSIONS: There were sex-specific differences in many of the demographic and clinical measures in our ED psychiatric sample. These differences indicate a potential need for targeted education and service initiatives to promote better access to psychiatric services and treatment outcomes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".