What it means to have a body as a trans* person: an inductive, qualitative approach to understanding body image in trans* populations
Bibliographic record
Abstract
and non-binary people are individuals who do not identify with their sex assigned at birth. Body image, particularly body dissatisfaction, has played a major role in the conceptualization of what it means to be a transgender or nonbinary person, as evidenced in transgender diagnoses since the 1980s. However, little research exists where researchers ask trans* people to describe, in their own words, how they feel about their bodies. Further, even less research exists that does not interpret these findings through the lens of pathology. Researchers have not yet explored how trans* people think, feel, and perceive their bodies or how they conceptualize body image using an inductive approach. This study interviewed a diverse sample of 10 transgender and non-binary individuals across Canada to better understand how trans* people think and feel about their bodies and how they conceptualize body image. Using thematic analysis, six main themes and four subthemes were identified, revolving around both personal aspects of body image (e.g., evaluations of one’s body, decisions about gender-affirming care) as well as social aspects, such as the influence that the dominant, cisgender society and social spaces can have on a trans* person’s body image. The current findings support that body image is a highly individualized and highly variable experience influenced by several factors, including social factors such as discrimination and stigma. These findings also highlight a need to reassess mainstream narratives about body image for trans* people, particularly the reliance on body dissatisfaction, as well as a need for body image measures conceptualized, designed for, and sensitive to the unique needs of transgender and nonbinary populations.
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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 | 0.021 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".