A call for increased measurement of eating disorders and disordered eating in federal surveillance in Canada
Bibliographic record
Abstract
Eating disorders (EDs) and disordered eating present a significant health burden given their prevalence and associated health risks; however, there are notable gaps in population-level surveillance of EDs and disordered eating in Canada. These data gaps limit our understanding of the scope of the problem and present challenges to monitoring trends in EDs and disordered eating in response to changing health and policy contexts, such as the COVID-19 pandemic. We screened Canadian federal health surveillance surveys to identify measures of ED diagnosis, engagement in disordered eating behaviours (e.g. binge eating, self-induced vomiting) and related constructs (e.g. weight perception, body satisfaction). Among adults, there was a 10-year gap in ED measurement, and there has been no assessment of engagement in any type of disordered eating behaviours. Among children and adolescents, there have been recent improvements in the measurement of disordered eating behaviours, but there are no surveys that include measures of binge eating, the most common disordered eating behaviour. National surveillance data assessing EDs and disordered eating are necessary to quantify their burden, assess trends in relation to evolving health and policy contexts and identify individuals who face barriers to seeking treatment services. We conclude by providing recommendations for constructs that should be measured, as well as guidelines for measurement development in conjunction with community members and clinical and research experts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".