Pediatric emergency mental health presentations during early COVID-19: Comparing virtual and in-person presentations
Bibliographic record
Abstract
Purpose: Increased mental health (MH) needs during the COVID-19 pandemic led to the implementation of a novel pediatric Emergency Department Virtual Care (EDVC) service. Our study aimed to describe the pediatric MH patient population that used EDVC by comparing patient-specific factors of those who obtained services virtually to those seen in-person. Method: This retrospective chart review was conducted at a pediatric hospital in Eastern Ontario. Children and youth (aged 3–17) who received virtual or in-person emergency MH services from May to December 2020 were included. Patient demographics, clinical presentation details and disposition were compared between the virtual and in-person groups. Data was analyzed using descriptive statistics. Results: 1104 youth (96.1%) utilized the in-person ED for MH concerns; 45 (3.9%) used EDVC. In-person youth had a higher level of perceived risk (78.9% vs. 41.9%) and were more likely to present with concerns of depression, suicidal ideation, self-harm, or laceration (46.1% vs. 35.6%). Anxiety/situational crises or behavioural issues were more likely to present virtually. Eight patients (17.8%) were redirected to the ED from EDVC. Conclusions: Several patient-specific factors varied between youth seen in-person or virtually for MH concern. Study results can assist with the design and implementation of virtual MH care platforms.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".