Bibliographic record
Abstract
Gender transition is undertaken to improve the well-being of people suffering from gender dysphoria. However, some have argued that the evidence supporting medical interventions for gender transition (e.g., hormonal therapies and surgery) is weak and inconclusive, and an increasing number of people have come forward recently to share their experiences of transition regret and detransition. In this essay, I discuss emerging clinical and research issues related to transition regret and detransition with the aim of arming clinicians with the latest information so they can support patients navigating the challenges of regret and detransition. I begin by describing recent changes in the epidemiology of gender dysphoria, conceptualization of transgender identification, and models of care. I then discuss the potential impact of these changes on regret and detransition; the prevalence of desistance, regret, and detransition; reasons for detransition; and medical and mental healthcare needs of detransitioners. Although recent data have shed light on a complex range of experiences that lead people to detransition, research remains very much in its infancy. Little is known about the medical and mental healthcare needs of these patients, and there is currently no guidance on best practices for clinicians involved in their care. Moreover, the term detransition can hold a wide array of possible meanings for transgender-identifying people, detransitioners, and researchers, leading to inconsistences in its usage. Moving forward, minimizing harm will require conducting robust research, challenging fundamental assumptions, scrutinizing of practice patterns, and embracing debate.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".