Promoting a positive school climate for sexual and gender minority youth through a systems approach: A theory-informed qualitative study.
Bibliographic record
Abstract
as an organizing analytical framework, to explore determinants of school climate for LGBT youth and strategies for intervention. In-depth, semistructured interviews with 16 key informants, including teachers, school staff, administrators, frontline community providers, and experts on bullying victimization of LGBT youth, illustrate reciprocal and multilevel factors that produce school climates, which in turn foster or prevent bullying of LGBT youth. Not only do distal factors (e.g., LGBT-affirmative legislation, targeted resource allocation for LGBT programming) impact school microsystems, but proximal factors in the microsystem, including enacted homophobia and transphobia through multilateral interpersonal interactions, also influence meso- and macrolevel phenomena, such as the values and mission of the school. Participants recommended multilateral interventions and training that address both proximal and distal contexts of school social ecologies, including teacher-student, peer-to-peer (e.g., gay-straight alliances), and teacher-administrator interactions; behavioral health professional roles and responsibilities; school curricula and libraries; school-board engagement with individual schools; LGBT-inclusive policies; targeted resource allocation; and systemwide accountability. Positive school climates for LGBT youth are promoted through multilevel and reciprocal interventions that support social, psychological, and physical safety not just for LGBT students but for all students. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".