Trends and disparities in disordered eating among heterosexual and sexual minority adolescents
Bibliographic record
Abstract
OBJECTIVE: Disordered eating has decreased for all youth over time, but studies have not focused specifically on lesbian, gay, and bisexual (LGB) youth. Research has found that LGB youth report disordered eating behaviors more often compared to their heterosexual counterparts, but no studies have documented trends over time for LGB youth and considered whether these disparities are narrowing or widening across sexual orientation groups. METHOD: We use pooled data from the 1999 to 2013 Massachusetts Youth Risk Behavior Surveys (N = 26,002) to investigate trends in purging, fasting, and using diet pills to lose or control weight for heterosexual and sexual minority youth. We used crosstabs, logistic regression, and interactions in regression models, stratified by sex. RESULTS: The prevalence of disordered eating has decreased on all three measures across nearly all groups of heterosexual and sexual minority youth. However, we found disparities in reported disordered eating behaviors for LGB youth persisted across all survey years, with LGB students reporting significantly higher prevalence of disordered eating than heterosexuals. The disparities in fasting to control weight widened between the first and last survey waves between lesbian adolescents and heterosexual females. DISCUSSION: The significant reductions over time in prevalence of disordered eating among some youth are encouraging, but the disparities remain. Indeed, the increasing prevalence of fasting, diet pill use, and purging to control weight among lesbians may warrant targeted prevention and intervention programs. © 2016 Wiley Periodicals, Inc. (Int J Eat Disord 2017; 50:22-31).
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".