Variation in admission rates between psychiatrists on call in a university teaching hospital
Bibliographic record
Abstract
Hospital-based physicians must routinely decide whether patients receiving care in the emergency room require admission to an acute care bed. We endeavoured to understand clinician-related factors that influence the decision to admit. We retrospectively examined data collected between August 1, 2013 and July 31, 2015 for patients triaged as mental health assessments in the emergency department of a university teaching hospital. We identified 1530 unique cases who had been reviewed by the staff psychiatrist for a decision on whether to admit to an acute care bed. Patient and physician characteristics were analyzed by standard descriptive methods, comparative statistics (Chi square and analysis of variance) and regression analyses using SPSS version 24.0 (IBM Corp. Armonk, NY, USA). There were no differences in patient characteristics in the clinical encounters reviewed by different staff psychiatrists. The physician factor found significant in deciding whether to admit the patient was assignment to PES (psychiatric emergency services). This appeared to be the only physician variable impacting the decision to admit a patient with PES psychiatrists admitting less often than their colleagues ( p = 0.018, Table 3 ). The effect size of the variable in terms of odds ratio was 0.592. Training and practice in emergency psychiatry lead to lower admission rates when these clinicians are on call. Training in emergency psychiatry for all psychiatrists participating in a call pool may result in lowered admission rates.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".