Examination of the impact of COVID-19 public health quarantine measures on acute mental health care services: A retrospective observational study
Bibliographic record
Abstract
This study assesses for the impact of Covid-19 public health quarantine measures on acute care psychiatric admissions, by comparing admission data from the quarantine period to a comparator period. A chart review was conducted for all admissions to an urban acute care psychiatric centre from Mar 22 - June 5 2020 (quarantine) and January 5 - Mar 21 2020 (comparator). Data was collected on the number of admissions, demographics, patients' psychiatric history, characteristics of admissions, discharge information, patients' substance use and social factors. Data was analyzed using a student's t-test for continuous variables and Chi squared analyses for categorical variables. Results demonstrated 185 admissions during quarantine and 190 during the comparator, with no significant differences in the distribution of admissions across time periods. There was a significantly greater frequency of admissions in the 35-44 age bracket and admissions involving substance use during quarantine. Additionally, admissions during quarantine were significantly shorter, with increased frequency of involuntary status and use of seclusion. The data suggests a vulnerability specific to individuals in their 30-40s during quarantine and demonstrates a need to better understand factors impacting this group. It also suggests that quarantine is associated with changes to substance use, potentiating high acuity illness requiring admission.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".