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Record W4214915346 · doi:10.1111/1747-0080.12727

Exploring the influence of gender dysphoria in eating disorders among gender diverse individuals

2022· article· en· W4214915346 on OpenAlexaff
Phillip Joy, Megan White, Shaleen Jones

Bibliographic record

VenueNutrition & Dietetics · 2022
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsTechnical University of Nova ScotiaMount Saint Vincent University
Fundersnot available
KeywordsGender dysphoriaEating disordersThematic analysisPsychologyClinical psychologyDysphoriaDisordered eatingGender identityTransgenderPsychiatryMedicineQualitative researchAnxietySocial psychology

Abstract

fetched live from OpenAlex

AIM: Eating concerns, disordered eating and eating disorders have been noted to negatively impact the health and wellbeing of sexual and gender diverse individuals. The aim of this study was to explore the experiences of gender diverse Canadians accessing treatment for eating disorders or disordered eating concerns to gain a deeper understanding of how dietitians and other health providers can provide gender affirming care. METHODS: Seven self-identifying gender diverse participants were recruited and took part in semi-structured interviews. Most participants identified as white. Data was analysed using thematic analysis. RESULTS: Four themes around gender dysphoria were constructed from the data, including gender dysphoria and eating disorders, barriers to accessing eating disorder treatments, harmful eating disorder treatment strategies and suggestions for eating disorder programmers and health professionals. CONCLUSION: Gender dysphoria considerations were believed to be lacking in traditional eating disorder treatment programs. Participants saw the need for more awareness and training in this area for dietitians and other health professionals.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.580

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.096
GPT teacher head0.313
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations26
Published2022
Admission routes1
Has abstractyes

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