An Ecological Systems Approach to Understanding Risk and Promoting Resilience Among Trans and Gender-Diverse Youth
Bibliographic record
Abstract
The present research investigated the experiences of risk and resilience for trans and gender-diverse youth. Ecological systems theory emphasizes how one is impacted by both proximal (e.g., peer interactions) and more distal (e.g., societal attitudes) environmental factors. Factors impacting trans and gender-diverse youth’s risk and resilience were explored at multiple levels of influence to achieve a comprehensive understanding of how to improve these youth’s well-being. Study 1 examined the relationship between broad, environmental marginalization levels and access to gender-affirming healthcare for trans youth. Study 1 compared marginalization levels in the areas surrounding trans youth (N = 298) who accessed gender-affirming healthcare services in Toronto, Canada, relative to marginalization levels in background populations. Compared with background populations, there was some evidence that areas where trans youth patients came from were biased toward less marginalization (e.g., less ethnic diversity). Study 2 was a qualitative study focused on asking Black, Indigenous, and People of Colour (BIPOC) trans youth (N = 12) about their experiences of risk and resilience. Despite facing multiple forms of marginalization, BIPOC trans youth have received limited research attention. Through qualitative coding, I identified four superordinate themes: accessing community connection and fostering belonging; navigating the healthcare system; personal journey with and relationship to gender identity; and others’ reactions to gender identity. Participants commented on day-to-day social interactions, interactions with societal institutions, and prevailing societal attitudes. Study 3 explored the possibility of changing societal attitudes in order to improve the experiences of trans and gender-diverse youth. Specifically, I investigated whether an empathy-based intervention could improve children’s (N = 186) attitudes towards gender-diverse peers and reduce gender stereotyping. Although the intervention was not effective, girl participants who displayed higher levels of trait empathy also displayed more positive ratings of target children. Also, among all children, gender stereotyping was related to more positive appraisals of gender-conforming (relative to gender-diverse) targets, suggesting that reducing stereotyping is an important strategy for improving children’s gender-related attitudes. Overall, the present research highlights a variety of factors at multiple levels of the environment that could be targeted to improve the well-being of trans and gender-diverse youth.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".