The impact of spirituality on eating disorder symptomatology in ethnically diverse Canadian women
Bibliographic record
Abstract
BACKGROUND: There is currently a gap in our knowledge of how eating disorder symptomatology is impacted by spirituality and religiosity. To date, studies examining the role of ethnicity in women's self-reported levels of eating disorder symptomatology have neglected the roles of spirituality and religiosity. AIMS: This study addresses this gap in the literature by investigating ethnicity, spirituality, religiosity, body shame, body mass index (BMI) and age in relation to eating disorder symptomatology in women. METHODS: A representative non-clinical sample of ethnically diverse Canadian women (N = 591) was surveyed. RESULTS: Younger women, particularly those with higher body shame, BMI and lower spirituality, reported more eating disorder symptomatology. Hispanic and Asian women had higher body shame and lower BMI compared to white women. Spirituality was more strongly related to eating disorder symptomatology than religiosity. CONCLUSIONS: This is the first study identifying interactive relationships between ethnicity, spirituality, body shame, BMI and age on eating disorder symptomatology in women. Particularly significant is that higher spirituality was related to a lower level of eating disorder symptomatology. These findings have important implications for treatment and women's physical and psychological health and wellness.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".