Shifting age of child eating disorder hospitalizations during the Covid‐19 pandemic
Bibliographic record
Abstract
BACKGROUND: We studied the effect of the Covid-19 pandemic on child eating disorder hospitalizations in Quebec, Canada. Quebec had one of the strictest lockdown measures targeting young people in North America. METHODS: We analyzed eating disorder hospitalizations in children aged 10-19 years before and during the pandemic. We used interrupted time series regression to assess trends in the monthly number of hospitalizations for anorexia nervosa, bulimia nervosa, and other eating disorders before the pandemic (April 2006 to February 2020), and during the first (March to August 2020) and second waves (September 2020 to March 2021). We determined the types of eating disorders requiring hospital treatment and identified the age, sex and socioeconomic subgroups that were most affected. RESULTS: Hospitalization rates for eating disorders increased during the first (6.5 per 10,000) and second waves (12.8 per 10,000) compared with the period before the pandemic (5.8 per 10,000). The increase occurred for anorexia nervosa as well as other types of eating disorders. The number of girls and boys aged 10-14 years admitted for eating disorders increased during wave 1. Wave 2 triggered an increase in eating disorder admissions among girls aged 15-19 years. Hospitalization rates increased earlier for advantaged than disadvantaged youth. CONCLUSIONS: The Covid-19 pandemic affected hospitalizations for anorexia nervosa as well as other eating disorders, beginning with girls aged 10-14 years during wave 1, followed by girls aged 15-19 years during wave 2. Boys aged 10-14 years were also affected, as well as both advantaged and disadvantaged youth.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".