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Record W3162109962 · doi:10.1002/eat.23537

The effect of misgendering on body dissatisfaction and dietary restraint in transgender individuals: Testing a <scp>Misgendering‐Congruence</scp> Process

2021· article· en· W3162109962 on OpenAlexaff
Linas Mitchell, Heather J. MacArthur, Kerstin K. Blomquist

Bibliographic record

VenueInternational Journal of Eating Disorders · 2021
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsTransgenderPsychologyEating disordersTransgender PersonDisordered eatingTransgender womenCongruence (geometry)Clinical psychologyDevelopmental psychologySocial psychologyMedicineMen who have sex with men

Abstract

fetched live from OpenAlex

OBJECTIVE: Despite research findings that transgender individuals have higher rates of body dissatisfaction and disordered eating than their cisgender peers, reasons for greater eating pathology remain unclear. We propose a Misgendering-Congruence Process by which being misgendered (i.e., labeled a gender other than that with which one identifies) could lead transgender individuals to feel greater incongruence between their bodies and internal identities, which in turn leads to body dissatisfaction and efforts to bring one's body in line with one's identified gender by engaging in weight and shape control behaviors such as dietary restraint. METHOD: One hundred and thirty transgender individuals completed measures of misgendering frequency, transgender congruence, body dissatisfaction, and dietary restraint. RESULTS: Mediation analyses provided preliminary support for the Misgendering-Congruence Process when conducted with the overall sample as well as with transgender subgroups: transgender women (n = 41), transgender men (n = 42), and nonbinary transgender individuals (n = 47). DISCUSSION: Social recognition of transgender people's gender identities appears to play a unique role in their body satisfaction and restrained eating behaviors.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.049
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.024
GPT teacher head0.340
Teacher spread0.316 · 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

Citations38
Published2021
Admission routes1
Has abstractyes

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