Stories of clinical care in transgender and nonbinary individuals with eating pathology: a scoping review
Bibliographic record
Abstract
BACKGROUND: Transgender and nonbinary (TGNB) individuals are at an increased risk for developing eating disorders (EDs) and ED-related symptoms. Despite this heightened vulnerability, research on clinical interventions is limited. This scoping review aims to map the extent and type of existing evidence related to clinical care and interventions for TGNB individuals with eating pathologies, while also focusing on the process and course of treatment at the individual level. METHODS: A scoping review was conducted following the PRISMA-ScR guidelines. PubMed/Web of Science/(EBSCO)PsycINFO was searched for studies on TGNB individuals with EDs/ED-related symptoms published until 27/06/2023. We included primary research studies with detailed information on treatment and clinical course (protocol: https://osf.io/crhga). RESULTS: Twenty-one articles encompassing 32 case reports were included. The results were organized into five timeframes: studies published before 2004 (k = 2); between 2004 and 2008 (k = 1); 2009-2013 (k = 0); 2014-2018 (k = 10); and 2019-2023 (k = 8). Interventions ranged from psychotherapy, gender-affirming hormones and surgery, nutritional counseling, to pharmacological treatments. Reported outcomes varied, with some studies showing improvements in body dissatisfaction and ED symptoms' reduction, while others highlighted clinical challenges such as frequent relapses and co-existing mental health conditions. CONCLUSION: This scoping review highlights the heterogeneity of stories of clinical care in TGNB individuals with eating pathologies, warranting individualized treatment approaches. Early studies often pathologized gender identity and used non-affirming language, whereas more recent studies emphasize inclusive, gender-affirming approaches. This evolution reflects a growing recognition of the unique challenges faced by TGNB individuals who seek help for EDs. Future research should overcome barriers to accessing care.
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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 | 0.019 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".