Bibliographic record
Abstract
Objectives. To determine whether the emergency psychiatric treatment of patients with psychosis varies with immigrant status and ethnicity. Methods. Data on immigrant and ethnic status of psychotic patients admitted in 1999 were extracted from records of a general hospital in Montreal. Of the 217 subjects, 97 (44.7%) were immigrants, 125 were Euro-Canadian (57.6%),39 were Asian (18.0%), and 27 were Black (12.4%). All Asians and most Blacks (87%) were immigrants. Measures of emergency psychiatric treatment included use of seclusion, restraints, and medication in the emergency department. Multiple regression models examined the relationship of immigrant status and ethnicity to emergency psychiatric treatment controlling for age, gender, patient height and weight, and mode of emergency department admission (coercive versus non-coercive). Results. Immigrant status and Asian ethnicity were not associated with emergency treatment measures. Coercive mode of emergency department admission (i.e. by police or ambulance) predicted use of seclusion (p < .001) and restraints (p < .05), but being Black was independently and positively associated with received dose of emergency antipsychotic (p < .05). Being Black was also positively associated with police or ambulance contact prior to emergency department presentation (p < .01). Conclusion. While some aspects of the emergency treatment of psychosis seem to occur as a consequence of the mode of admission, the administration of antipsychotic medication may be motivated by patient ethnicity. These results point to the need for training of emergency department staff to reduce potential bias in treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".