School Factors Strongly Impact Transgender and Non-Binary Youths’ Well-Being
Bibliographic record
Abstract
BACKGROUND: School plays an important role in transgender and non-binary (TNB) youths' life and well-being. The aim of this study was to gain a better understanding of how the lived experiences, gender affirmation and challenges encountered by TNB youths in the school setting affect their well-being. METHOD: Our study was a qualitative secondary data analysis, based on the interviews of 12 Canadian TNB youths aged 15-17 years old. RESULTS: We found that TNB students' well-being was closely related to the acknowledgment of gender identity at school. Several factors, including school socio-cultural environment, teachers' and peers' attitudes and behaviours, school physical environments and the respect of confidentiality of gender identity were all found to impact TNB students' well-being. To face adversity related to some of these factors, TNB youths used several contextually driven strategies such as compromising, educating, and sensitizing others about gender diversity and avoiding certain people or situations. CONCLUSION: Our results highlight the important influence of school climate and culture, as well as teachers', school personnel's and peers' behaviours and attitudes on TNB youths' well-being. Our findings can guide future interventions to help schools become more inclusive and supportive of gender diversity.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".