The once revolutionary ‘Dutch model’ of adolescent transgender care, which includes the use of puberty blockers, is currently being challenged. Help us to analyze the prospectively collected rich data of our unique Amsterdam Cohort of Gender Dysphoria Adolescent (ACOG-Ado) and address with us the current dilemmas in adolescent transgender care.
The
project Clinics providing medical transgender care for youth are confronted with sharp increases in referrals within a context where critics have become more vocal, expressing concerns about decision-making ability of adolescents and the limited evidence base for the current care model.
This project will 1) investigate if childhood versus adolescent onset developmental pathways can be distinguished and correlate with treatment outcomes, 2) gain insight in optimal decision-making processes in gender affirmative treatment for transgender adolescents, parents and health care providers and 3) integrate findings from aim 1 and aim 2 and co-create an optimal decision-making framework for transgender care for adolescents.
The project is a mixed-method study consisting of three work packages.
As a PhD you will, focus on aim 1 while using existing baseline data (n = 2000+) and newly collected follow-up data during treatment (1 year blockers and 1 year hormones) and in young adulthood (n = 600+), and examine whether distinct developmental pathways can be distinguished which correlate with treatment outcomes. Apart from descriptive analyses, you will use data-driven approaches. You will collaborate with the larger research team that includes a qualitatively participatory approach. Your quantitatively collected new data evidence with the normative ethical knowledge will lead to an optimal decision-making framework whereupon an advanced adolescent transgender care model can be based.
You main responsibilities are:
- The collection and storage of questionnaire data on the different times (baseline, during treatment on blockers and on hormones, young adulthood)
- Collaborate with qualitative researchers and be involved in design of research measures and
- Perform general descriptive mean-outcome group comparisons to devaluate the overall effectiveness of treatment (logistic regression analyses, serial ANOVA’s or MANOVA)
- Apply data driven approaches (e.g. a network approach) to gain insight into multivariate dependencies and generate hypotheses about putative relations concerning baseline characteristics, developmental pathways and treatment outcomes.