Impact of SARS-CoV-2 pandemic on the mental and physical health of children enrolled in a paediatric weight management clinic
Bibliographic record
Abstract
Background: Obesity is a global public health concern. Given the widespread disruption caused by the SARS-CoV-2 pandemic, it is important to evaluate its impact on children with chronic health conditions. This study examines the health of paediatric patients with obesity enrolled in a tertiary hospital weight management program, before and 1 year into the COVID-19 pandemic. Methods: This is a retrospective chart review of patients aged 2 to 17 years enrolled in a paediatric weight management clinic. Mental health outcomes (i.e., new referrals to psychologist, social work, eating disorder program, incidence of dysregulated eating, suicidal ideation, and/or self-harm) and physical health (anthropometric measures) were compared before and 1 year into the pandemic. Results: Among the 334 children seen in either period, there was an increase in referrals to psychologist (12.4% versus 26.5%; P=0.002) and the composite mental health outcome (17.2% versus 30.2%; P=0.005) during the pandemic compared with pre-pandemic. In a subset of children (n=30) with anthropometric measures in both periods, there was a lower rate of decline in BMIz score (-1.5 [2.00] versus -0.3 [0.73]/year; P=0.002) and an increase in adiposity (-0.8 [4.64] versus 2.7 [5.54]%/year; P=0.043) during the pandemic. Discussion: The pandemic has impacted the mental and physical health of children with obesity engaged in a weight management clinic. While our study provides evidence of a negative impact on mental health outcomes and less improvement in anthropometric measures, future research when patients return to in-person care will enable further examination of our findings with additional objective measures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".