The effect of misgendering on body dissatisfaction and dietary restraint in transgender individuals: Testing a <scp>Misgendering‐Congruence</scp> Process
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".