Disordered eating behaviors among transgender youth: Probability profiles from risk and protective factors
Bibliographic record
Abstract
PURPOSE: Research has documented high rates of disordered eating for lesbian, gay, and bisexual youth, but prevalence and patterns of disordered eating among transgender youth remain unexplored. This is despite unique challenges faced by this group, including gender-related body image and the use of hormones. We explore the relationship between disordered eating and risk and protective factors for transgender youth. METHODS: An online survey of 923 transgender youth (aged 14-25) across Canada was conducted, primarily using measures from existing youth health surveys. Analyses were stratified by gender identity and included logistic regressions with probability profiles to illustrate combinations of risk and protective factors for eating disordered behaviors. RESULTS: Enacted stigma (the higher rates of harassment and discrimination sexual minority youth experience) was linked to higher odds of reported past year binge eating and fasting or vomiting to lose weight, while protective factors, including family connectedness, school connectedness, caring friends, and social support, were linked to lower odds of past year disordered eating. Youth with the highest levels of enacted stigma and no protective factors had high probabilities of past year eating disordered behaviors. DISCUSSION: Our study found high prevalence of disorders. Risk for these behaviors was linked to stigma and violence exposure, but offset by social supports. Health professionals should assess transgender youth for disordered eating behaviors and supportive resources. © 2016 Wiley Periodicals, Inc.(Int J Eat Disord 2017; 50:515-522).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".