Navigating the disclosure landscape: Parents' perspectives on healthcare professionals' role in supporting intersex children and families
Bibliographic record
Abstract
Intersex, an umbrella term, describes individuals with sex characteristics that cannot be exclusively categorized into binary definitions of male or female. The intersex community faces a lack of social visibility perpetuated by a history of medical discrimination and pathologization shaped by "normalizing" genital surgeries without the child's consent. Despite efforts to reform clinical practice, there remains a paucity of research centering the needs of the intersex community and their families. This study explored parents' perspectives on how healthcare professionals (HCPs), such as genetics professionals, can provide patient-centered education and support when parents first learn of their child's intersex variation, with the aim of offering recommendations to HCPs to promote parental adjustment and protect intersex children's right to autonomy. Thirteen qualitative semi-structured interviews were conducted with 14 parents of intersex children. Through reflexive thematic analysis, under the framework of an agency-based approach to intersex health, thematic categories were inductively conceptualized, including barriers and facilitators to HCPs' sensitivity and to parental adjustment in the early disclosure environment. Barriers to HCPs' sensitivity were imposed by educational, religious, or medical institutions, along with sociocultural prejudices and pathologizing language. Barriers to parental adaptation included uncertainty regarding their child's future, sociocultural gender norms, and unsuitable information provision. Incorporating parental needs in the disclosure environment can facilitate familial acceptance, including normalization of variations of sex characteristics (VSCs), enhanced medical education, facilitation of patients' navigation, and prioritization of parents' social support needs.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".