Changes in psychiatric admissions in the first year of COVID-19 in Ontario, Canada
Bibliographic record
Abstract
Abstract Background Several studies showed strong evidence that the COVID-19 pandemic disrupted mental health service use, with changes in emergency department visits, and psychiatric hospital admissions. It is not clear, however, whether the pandemic caused an increase or decrease in use of services for people with different diagnoses and symptoms. Methods We used data from all individuals admitted to psychiatric units in Ontario, Canada (259,620 individuals) from January 1st 2015 to December 31st, 2020 and compared the number of admissions, length of stay, symptoms, and clinical characteristics of this population in 2020 to the average of those who were admitted between 2015 and 2019. Results Total number of admissions declined sharply (44%) during the first lockdown period but returned to pre-pandemic levels within about 2 months. This trend, however, was not observed for all types of mental health problems. Admissions for symptoms such as risk of harm to others and addictions were consistently higher after the first wave in May 2020 compared to the same month in the previous 5 years, while symptoms such as social withdrawal, and depression were consistently lower. Conclusion Taken together, these results suggest that the impact of the pandemic on the use of mental health services were symptom-specific, which is likely a result of the heterogeneity of mental health problems within this population. This variation in the changes in psychiatry admissions for patients with different clinical profiles should be considered when preparing for future service interruptions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".