The economic impact of eating disorders in children and youth in Canada: a call to action to improve youth eating disorder research and care
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has led to an unprecedented rise in rates and symptoms of eating disorders among Canadian youth. To date, there is a lack of national surveillance and costing data in Canada to inform policymakers and healthcare leaders on how to best address the surge in new and existing cases. This has resulted in the Canadian healthcare system being unprepared to adequately respond to the increased needs. Therefore, clinicians, researchers, policymakers, decision-makers, and community organizations across Canada are collaborating to compare pre-and post-pandemic costing data from national and province-level healthcare systems in an effort to address this gap. Results from this economic cost analysis will be an important first step in informing and guiding policy on possible adaptations to services to better fulfill the needs of youth with eating disorders in Canada. We highlight how gaps in surveillance and costing data can impact the field of eating disorders in an international context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".