Eating disorders and substance abuse in Canadian women: a national study
Bibliographic record
Abstract
AIMS: This study aimed to examine the comorbidity between eating disorders and substance use in a large nationally representative sample of Canadian adult women. Recent as well as life-time measures of substance use were used. DESIGN: The research was based on secondary analyses of data collected, using multi-stage stratified probability sampling, by Statistics Canada in the Mental Health and Well-being cycle 1.2 of the Canadian Community Health Survey (CCHS). MEASUREMENTS: The Eating Attitude Test (EAT-26) was used to measure risk of eating disorders. Alcohol use, dependence and interference, and illicit drug use, dependence and interference were measured using relevant modules from the short form of the Composite International Diagnostic Interview (CIDI-SF). PARTICIPANTS: Data on a nationally representative sample of Canadian adult women, grouped into three age groups, were used for this research. FINDINGS: Alcohol dependence and alcohol interference were associated significantly with the risk for an eating disorder in the three adult age groups. Significant associations were also found in the three age groups between risk for an eating disorder and the life-time abuse of and dependence on illicit drugs. Significant associations were found in the 15-24 and 25-44-year age groups when the 12-month time-frame was used. CONCLUSIONS: The study findings support the call for the development of short screening instruments for adult women with eating disorders and substance abuse, as well as for the development of treatment strategies that address the co-occurrence of eating disorders and substance use
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".