Gender Differences and Characteristics of Psychiatric Patients assessed in The Emergency Department of a Regional Hospital in Canada
Bibliographic record
Abstract
Background Epidemiological studies of emergency room (ER) psychiatric settings can help monitor unmet needs and improve the quality of care. Objectives To characterize the patients presenting to emergency department with psychiatric complaints in a medium sized health centre in oil sands region of the Northern alberta. Methods information on a data assessment tool designed to capture all relevant demographic and clinical characteristics of psychiatric patients in the ER was compiled as part of a clinical audit process. Results Overall, 477 patients were assessed by the psychiatric team over the 12 month period, comprising 230 (48.2%) males and 247 (51.8%) females. There was a fairly balanced distribution by age, ethnic background, and relationship status between the male and female patients. 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 the 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. approximately half of all the patients had an impaired clinical insight. Majority of the patients had a G aF score of 70 or less. Nearly one in five patients were admitted for inpatient treatment with a significantly higher proportion of male patients being admitted involuntarily. Conclusion There are sex-specific differences in many of the demographic and clinical measures collected in our ER psychiatric sample. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".