“I feel good, I feel comfortable, I feel at home”: Understanding the role of positive body image manifestations, body neutrality and functionality appreciation for trans men
Bibliographic record
Abstract
Body image concerns are prevalent among trans men, yet research has primarily focused on appearance-based dissatisfaction rather than alternative frameworks. There is also limited research directly and purposefully exploring the experiences of trans men. This qualitative study explored how trans men navigate body image, gender congruence, and quality of life, using reflexive thematic analysis of semi-structured interviews with 20 participants from seven Global North countries. Findings highlight how societal masculinity norms initially reinforced body dissatisfaction and gender incongruence, leading participants to internalise rigid body ideals. However, over time, many redefined masculinity in ways that prioritised authenticity and self-acceptance, particularly through medical transition. Gender-affirming care played a key role in reducing distress associated with gender incongruence, enabling trans men to disengage from unrealistic body ideals. Many participants described a shift towards body neutrality and functionality appreciation, reframing their relationship with their bodies by focusing on what they could do rather than how they looked. These findings suggest that body image interventions and gender-affirming care may benefit from integrating approaches beyond appearance-based frameworks. Given the novelty of functionality appreciation and body neutrality in trans men’s body image research, future studies should explore their role in psychosocial well-being and long-term adjustment.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".