Adapting cognitive remediation group therapy: A community-based, focus group study
Bibliographic record
Abstract
Abstract Cognitive impairment is a significant health issue for people aging with HIV/AIDS. With pharmacological treatment lacking, psychosocial group therapies may best help people aging with HIV who experience cognitive challenges cope with their symptoms. The COVID-19 pandemic revealed how in-person group therapies need adaptation for hybrid or online delivery. Peer-led focus groups discussed adapting cognitive remediation group therapy (CRGT) as a hybrid or online intervention. CRGT combines mindfulness-based stress reduction and brain training activities. Purposive sampling was used to recruit people aging with HIV (40+) who self-identified cognitive concerns and resided in one of two Canadian provinces. Thematic content analysis was employed on transcripts by seven independent coders. Ten, two-hour focus groups were conducted between August and November 2022. Participant (n=45) demographics included age (M=53.22, SD=7.62), gender (45% women, 42% men, 13% trans/non-binary), sexual orientation (42% gay, 40% heterosexual, 18% other), ethnicity (45% White, 33% Black, 13% Indigenous, 9% mixed-race), and employment (33% employed, 67% retired/disability), and all on treatment and retained in care. Overall, participants responded favourably to CRGT’s modalities. Alongside support for its continued implementation in-person, participants requested online synchronous and online asynchronous formats. Preferred intervention facilitators were peers and mental health professionals. We also discuss how to adapt psychosocial HIV therapies for technology-mediated delivery, with consideration for behavioral HIV research.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".