Does physician compensation for declaration of involuntary status increase the likelihood of involuntary admission? A population-level cross-sectional linked administrative database study
Bibliographic record
Abstract
BACKGROUND: There is substantial variability in involuntary psychiatric admission rates across countries and sub-regions within countries that are not fully explained by patient-level factors. We sought to examine whether in a government-funded health care system, physician payments for filling forms related to an involuntary psychiatric hospitalization were associated with the likelihood of an involuntary admission. METHODS: This is a population-based, cross-sectional study in Ontario, Canada of all adult psychiatric inpatients in Ontario (2009-2015, n = 122 851). We examined the association between the proportion of standardized forms for involuntary admissions that were financially compensated and the odds of a patient being involuntarily admitted. We controlled for socio-demographic characteristics, clinical severity, past-health care system utilization and system resource factors. RESULTS: Involuntary admission rates increased from the lowest (Q1, 70.8%) to the highest (Q5, 81.4%) emergency department (ED) quintiles of payment, with the odds of involuntary admission in Q5 being nearly significantly higher than the odds of involuntary admission in Q1 after adjustment (aOR 1.73, 95% CI 0.99-3.01). With payment proportion measured as a continuous variable, the odds of involuntary admission increased by 1.14 (95% CI 1.03-1.27) for each 10% absolute increase in the proportion of financially compensated forms at that ED. CONCLUSIONS: We found that involuntary admission was more likely to occur at EDs with increasing likelihood of financial compensation for invoking involuntary status. This highlights the need to better understand how physician compensation relates to the ethical balance between the right to safety and autonomy for some of the world's most vulnerable patients.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| 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) | 0.002 | 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".