Extrafamilial stressors in families of transgender adolescents referred for gender-affirming medical care: a mixed-methods analysis
Bibliographic record
Abstract
Transgender and nonbinary (TNB) adolescents and their families often experience trans-specific, extrafamilial stressors, which may increase when adolescents come out and try to access gender-affirming medical care. While studies have described such stressors, it is unclear whether distinct underlying patterns of stressor experiences exist, shaping family experience. 159 adolescent–parent dyads attending an initial hormone appointment for gender-affirming medical care at any of 10 clinics in Canada reported on trans-specific, extrafamilial stressor experiences in Trans Youth CAN! Latent class analysis (LCA) assessed underlying patterns; parent and family characteristics were then described for each stressor class in the final model. LCA interpretation was supplemented with thematic analysis of qualitative interviews with 36 parents at 3 of the clinics from the Stories of Care study. The optimal model had four stressor classes: “Low Disruption, Some Advocacy” (estimated 30.4%); “Some Disruption, Some Advocacy” (9.8%); “Low Disruption, Low Advocacy” (55.7%); and “Major Disruption, High Advocacy” (4.1%). Family characteristics suggested a heterogeneous sample, with differing proportions of sociodemographic and family characteristics across stressor classes. Quotations from parent interviews in Stories of Care supported the four-class stressor model. Families of TNB adolescents accessing gender-affirming medical care may experience trans-specific, extrafamilial stressors according to four latent class groupings.
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 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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".