Adult Psychiatric Hospitalizations in Ontario, Canada Before and During the COVID-19 Pandemic
Bibliographic record
Abstract
OBJECTIVE: The impacts of the COVID-19 pandemic on psychiatric hospitalizations in Ontario are unknown. The purpose of this study was to identify changes to volumes and characteristics of psychiatric hospitalizations in Ontario during the COVID-19 pandemic. METHODS: A time series analysis was done using psychiatric hospitalizations with admissions dates from July 2017 to September 2021 identified from provincial health administrative data. Variables included monthly volumes of hospitalizations as well as proportions of stays <3 days and involuntary admissions, overall and by diagnosis (mood, psychotic, addiction, and other disorders). Changes to trends during the pandemic were tested using linear regression. RESULTS: A total of 236,634 psychiatric hospitalizations were identified. Volumes decreased in the first few months of the pandemic before returning to prepandemic volumes by May 2020. However, monthly hospitalizations for psychotic disorders increased by ∼9% compared to the prepandemic period and remained elevated thereafter. Short stays and involuntary admissions increased by approximately 2% and 7%, respectively, before trending downwards. CONCLUSION: Psychiatric hospitalizations quickly stabilized in response to the COVID-19 pandemic. However, evidence suggested a shift towards a more severe presentation during this period.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".