Empirically Supported Interventions for Sexual and Gender Minority Youth
Bibliographic record
Abstract
When empirically supported treatments (ESTs) are effectively adapted for use with minority populations, they may be more efficacious. As such, there is a need to adapt existing ESTs for use with diverse sexual and gender minority youth (SGMY). The unique bias-based challenges faced by SGMY require the integration of affirmative practices into ESTs to effectively address the specific needs of this underserved group of youth. The primary purpose of the authors in this article is to present a clearly articulated stakeholder driven model for developing an affirmative adapted version of cognitive behavioral therapy (CBT) for use with diverse SGMY. The authors' approach to adaptation follows the "adapt and evaluate" framework for enhancing cultural congruence of interventions for minority groups. A community based participatory research approach, consistent with a stakeholder driven process, is utilized to develop the intervention from the ground up through the voices of the target community. Researchers conducted 3 focus groups with culturally diverse SGMY to explore salient aspects of youths' cultural and SGM identities in order to inform the intervention and ensure its applicability to a wide range of SGMY. Focus group data is analyzed and integrated into an existing group-based CBT intervention. The following themes emerge as critical to affirmative work with diverse SGMY: (1) the interplay between cultural norms, gender norms, sexual orientation, and gender identity; (2) the complex role of religious community within the lives of SGMY; and (3) consideration of extended family and cultural community as youth navigate their SGM identities.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".