LGBTQ + Affirmative CBT: a hierarchical linear model of longitudinal outcomes and mechanisms of change
Bibliographic record
Abstract
BACKGROUND: Sexual and gender diverse adolescents and young adults (SGDAYA) experience mental health disparities, yet few empirical investigations into the long-term impact of affirmative treatments on their well-being exist. METHODS: This study explored the longitudinal effects of a brief affirmative cognitive-behavioral therapy (CBT) group intervention (AFFIRM) on the depression and anxiety of SGDAYA (N = 202), as well as how pre-treatment and mid-intervention change mechanisms contributed to their improved mental health. Participants' age ranged from 14 to 29 years old at baseline (M = 22.12, SD = 4.60). Data were collected at four time points (pre-test, post-test, 6 months, 1 year) and analyzed using hierarchical linear models. RESULTS: Participants reported significant improvements in anxiety and depression from baseline to 1-year follow-up as well as increased engagement coping, social support, hope, and improved stress appraisal. SGDAYA, who appraised stress as a threat and had less ability to envision a hopeful future (hope pathway) at baseline, reported greater improvements in depression and anxiety; additionally, those who used more disengagement coping strategies prior to AFFIRM reported more reduction in depression. Participants with the most significant long-term improvement in depression reported (a) greater increases in their resources to deal with stress, (b) more uptake of engagement coping, and (c) improved hope pathway. CONCLUSIONS: This study suggests that an affirmative cognitive-behavioural group intervention designed for SGDAYA can have a long-term impact on their depression and anxiety and highlights the important role of engagement coping, social support, hope and cognitive appraisals on youth mental health. TRIAL REGISTRATION: AFFIRM was retrospectively registered as a clinical trial on March 24th, 2020 (identifier: NCT04318769).
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".