Ethnicity, socioeconomic status, and eating disorder symptomatology in Canada: implications for mental health care
Bibliographic record
Abstract
Purpose – There is a gap in the understanding of relationships between socioeconomic status (SES), urban-rural differences, ethnicity and eating disorder symptomatology. This gap has implications for access to treatment and the effectiveness of treatment. The paper aims to discuss these issues. Design/methodology/approach – Data are presented from a major Canadian survey, analyzing the impact of body mass index (BMI), urban-non-urban residency, income, and ethnicity on eating disorder symptomatology. Findings – One of the strongest findings is that high income non-White women expressed less eating disorder symptomatology than lower income non-White women. Research limitations/implications – Future research needs to consider how factors such as urban residency, exposure to Western “thinness” ideals, and income differentials impact non-White women. Practical implications – Effective treatment of ethnic minority women requires an appreciation of complicated effects of “culture clash,” income and BMI on eating disorder symptomatology. Originality/value – This study makes a unique contribution to the literature by examining relationships between SES (income) and eating disorder symptomatology in White and non-White Canadian women. The review of the scientific literature on ethnic differences in eating disorder symptomatology revealed a disparity gap in treatment. This disparity may be a by-product of bias and lack of understanding of gender or ethnic/cultural differences by practitioners.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".