A comparison of psychiatric inpatient admissions in youth before and during the COVID-19 pandemic.
Bibliographic record
Abstract
Background: The current understanding of the effect of COVID-19 on child and youth admissions to psychiatric inpatient units over time is limited, with conflicting findings and many studies focusing on the initial wave of the pandemic. Objectives: This study identified changes in psychiatric inpatient admissions, and reasons for admission, including suicidality and self-harm, before and during the COVID-19 pandemic. Method: , 2021. Pre-pandemic (before March 11, 2020) and during-pandemic (after March 11, 2020) trends of admissions were explored using a Bayesian structural time series model (BSTS). Results: The model revealed that overall admissions during the pandemic period exceeded what would have been predicted in the absence of a pandemic, a relative increase of 29%. Additionally, a rise in the total number of admissions due to self-harm and suicidality (29% increase), externalizing/behavioral issues (69% increase), and internalizing/emotional issues (28% increase) provided strong evidence of increased admissions compared to what might have been expected from pre-pandemic numbers. Conclusions: There was strong evidence of increases in psychiatric inpatient admissions during the COVID-19 pandemic compared to expected trends based on pre-pandemic data. To ensure accessible and continuous mental health supports and services for youth and their families during future pandemics, these findings highlight the need for rapid expanse of inpatient mental health services, similar to what occurred in many intensive care units across Canada.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".