AFFIRM Online: Utilising an Affirmative Cognitive–Behavioural Digital Intervention to Improve Mental Health, Access, and Engagement among LGBTQA+ Youth and Young Adults
Bibliographic record
Abstract
Digital mental health interventions may enable access to care for LGBTQA+ youth and young adults that face significant threats to their wellbeing. This study describes the preliminary efficacy of AFFIRM Online, an eight-session manualised affirmative cognitive behavioural group intervention delivered synchronously. Participants (Mage = 21.17; SD = 4.52) had a range of sexual (e.g., queer, lesbian, pansexual) and gender (e.g., non-binary, transgender, cisgender woman) identities. Compared to a waitlist control (n = 50), AFFIRM Online participants (n = 46) experienced significantly reduced depression (b = −5.30, p = 0.005, d = 0.60) and improved appraisal of stress as a challenge (b = 0.51, p = 0.005, d = 0.60) and having the resources to meet those challenges (b = 0.27, p = 0.059, d = 0.39) as well active coping (b = 0.36, p = 0.012, d = 0.54), emotional support (b = 0.38, p = 0.017, d = 0.51), instrumental support (b = 0.58, p < 0.001, d = 0.77), positive framing (b = 0.34, p = 0.046, d = 0.42), and planning (b = 0.41, p = 0.024, d = 0.49). Participants reported high acceptability. This study highlights the potential of digital interventions to impact LGBTQA+ youth mental health and explores the feasibility of digital mental health to support access and engagement of youth with a range of identities and needs (e.g., pandemic, lack of transportation, rural locations). Findings have implications for the design and delivery of digital interventions for marginalised youth and young adults.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".