A nuanced look into youth journeys of gender transition and detransition
Bibliographic record
Abstract
Abstract Some media have suggested that many youths who have previously completed a gender transition are “detransitioning”. Their experience is often framed around the idea of regrets but rare are the articles that provide a nuanced examination of their journey. This article presents the perspectives of youths who have detransitioned or discontinued a transition regarding their experiences and feelings on their journey from transition to detransition. Semi‐structured interviews were conducted with 20 youths between the age of 16 and 25 years who were recruited on social media and who transitioned and detransitioned or discontinued their transition. Data were analysed according to thematic analysis. Regrets and feelings of satisfaction can both coexist. The processes of transition and discontinuation or detransition appear to be non‐linear and participants do not necessarily return to a cisgender identity. Ambiguous loss theory is applied to frame youth experiences and feelings and to suggest way forward for intervention. Highlights This paper examines the experiences and feelings of youth on their journey from transition to detransition. Their journey is experienced as non‐linear, and often comprised mixed feelings and experiences about transition and detransition steps. Ambiguous loss theory allows a nuanced understanding of feelings and experiences of their journey from transition to detransition.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| 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".