Social determinants of involuntary psychiatric hospital admissions in Ontario, Canada
Bibliographic record
Abstract
Introduction In Ontario, Canada, patients may be admitted to the hospital involuntarily if they are deemed to be suffering from symptoms of a mental disorder that may result in imminent serious bodily harm to themselves or others, or that may cause serious physical impairment to themselves (e.g., inability to keep themselves safe and warm in the winter). This measure can be life-saving. However, in addition to ethical and human rights considerations, resorting to coercive admissions may be an indication that those who are suffering from mental illness are not able to access or receive timely and appropriate intervention. While recent studies have suggested that the rate of involuntary hospital admission may be increasing, data on social determinants of involuntary hospital admissions are limited. Objectives We examined social factors associated with involuntary admissions using a Canadian provincial database. Methods Binary logistic regression models were conducted to examine the associations between social factors (low income, indigeneity, rurality, housing type) and involuntary admissions, controlling for age, sex, and psychiatric diagnoses. Data from March 2019 to March 2021 was extracted from the Ontario Mental Health Reporting System admission dataset, comprising of a sample of 9,848 patients admitted to eight psychiatric hospitals in Ontario. Odds ratios and 95% confidence intervals are reported. Results In 2021, the proportion of involuntary patients increased significantly by 6.8 percentage points to 55.7% compared to the previous year (48.9%). Indigenous status (First Nations, Metis, Inuit) [1.75 (1.38-2.21) **], living in rural areas [2.78 (2.48-3.12)], living in assisted residence [1.41 (1.21-1.64) **], homelessness [1.63 (1.38-1.91) **], male sex [1.21 (1.10-1.33) **] and younger age [0.99 (0.98-0.99) **] were associated with involuntary admissions, while income was not a significant factor. Compared to a diagnosis of a psychotic disorder, substance use disorders [0.11 (0.10-0.13) **] and mood and anxiety disorders [0.32 (0.29-0.36) **] showed decreased odds of involuntary admission, while neurocognitive disorders increased the odds of involuntary admission [3.86 (2.91-5.11) **]. Conclusions Consistent with other findings, involuntary psychiatric hospital admissions in ON, Canada, have increased recently, which may in part be related to the pandemic. Rurality, indigenous status, and unstable housing have been found to be associated with involuntary admissions. The study findings support the need for better preventive and intervention strategies to serve vulnerable psychiatric patients, including addressing the social determinants of health such as housing, and increasing access to culturally competent and safe community-based mental health supports and services. Disclosure of Interest None Declared
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".