Experiences of disordered eating and exercise behaviors of neurodivergent, gender diverse youth: a scoping review
Bibliographic record
Abstract
Objective: neurodivergent. This article examines the existing literature that explores disordered eating in neurodivergent, gender diverse youth. Method: A scoping review was conducted using the Joanna Briggs Institute (JBI) guidelines for scoping reviews and Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) checklist. We searched Embase, Healthstar, MEDLINE, PsycINFO, Scopus, and Web of Science for relevant articles. Results: 31 articles from our searches met inclusion criteria. An additional five articles were found from reference lists of included articles, for a total of 36 articles. Following best practices for reporting for scoping reviews, we used a basic qualitative content analysis to organize the findings into the following three broad categories: (1) Rates and severity of feeding and eating disorders and neurodivergence of gender diverse youth; (2) Factors contributing to feeding and eating disorder or disordered eating behavior development in gender diverse, neurodivergent youth; and (3) Treatment outcomes. Discussion: Gender diverse, neurodivergent youth are at increased risk of developing feeding and eating disorders or disordered eating behaviors. Experiences of gender dysphoria, cognitive differences, and minority stress may contribute to this increased risk. While access to gender-affirming care is a necessary component of treatment, it does not appear to be sufficient for resolving feeding and eating disorders or disordered eating behaviors in this population.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".