Rate of adolescent inpatient admission for psychosis during the COVID‐19 pandemic: A retrospective chart review
Bibliographic record
Abstract
AIM: Given the concerns for mental health (MH) impacts on children and adolescents during the COVID-19 pandemic, as well as the relative paucity of research in this field, this retrospective study compares the rate of paediatric inpatient MH admissions for psychosis for a period of 11 months before and during the pandemic. METHODS: We used administrative data to compare the rate and clinical characteristics of patients (<18 years) admitted to a psychiatric inpatient unit for a psychotic illness before (March 17, 2019 to February 17, 2020) and during (March 17, 2020 to February 17, 2021) the COVID-19 pandemic. RESULTS: Results showed a 66% increase in inpatient psychosis admissions from pre-pandemic rates. More males were admitted with psychosis during the pandemic. Age and length of hospitalization were not significantly different across time periods. CONCLUSIONS: Results highlight the importance of accessible MH care for paediatric patients with psychosis during the pandemic.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".