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 whether these correlate with treatment outcomes, 2)
gain insight into 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 to co-create an optimal decision-making framework for transgender care for adolescents.
The project is a mixed-methods study consisting of three work packages.
As Postdoc researcher you will (while supervising a PhD and data-manager) focus on aim 1, 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+), to examine whether certain baseline factors and/or developmental pathways or networks correlate with certain treatment outcomes. In addition to descriptive analyses, you will use data-driven approaches.
You will collaborate with the larger research team that includes a qualitative participatory approach. Your new quantitative data evidence combined with the normative ethical knowledge will lead to an optimal decision-making framework upon which an advanced adolescent transgender care model can be based.
Your main responsibilities are:
- Supervise the collection and storage of questionnaire data on the different time points (baseline, during treatment on blockers and on hormones, young adulthood)';
- Collaborate with qualitative researchers and be involved in design of research measures';
- Perform and supervise general descriptive mean-outcome group comparisons to evaluate the overall effectiveness of treatment (e.g., 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 among baseline characteristics, developmental pathways and treatment outcomes.