Adapting Cognitive Remediation Group Therapy as an Online or Hybrid Intervention for People Aging With HIV and Cognitive Concerns: Focus Group Protocol
Bibliographic record
Abstract
Cognitive impairment is a significant health issue for people aging with HIV/AIDS. Cognitive challenges can include forgetfulness, trouble concentrating, and increasing struggles to learn new skills, all of which contribute to poorer mental health and decreased quality of life. Although there is no specific drug therapy that can reverse the brain impairment, group therapies may help people aging with HIV and cognitive challenges to better cope with their symptoms when combined with their usual medical treatment and follow-up. This community-based study will involve peer-led focus groups to discuss cognitive remediation group therapy – a combination of mindfulness-based stress reduction and brain training activities tested in a pilot randomized, controlled trial – as an in-person intervention for people aging with HIV in 2019. Via a brief demographic survey and technology-mediated focus groups ( n = 40) in Ontario and Saskatchewan, we will determine how the intervention could be adapted in an online or hybrid format considering the ongoing COVID-19 pandemic. Content analysis will be employed whereby a team of independent coders will code the focus group transcripts in line with the co-design framework and “Double Diamond” model of developing interventions, including intervention structure, content, and mode of delivery. Given the aging of the HIV population in Canada, increasing support will be required in addition to medical care to improve quality of life, and proactively address concerns about cognition. This protocol provides a roadmap for adapting in-person psychosocial interventions using community-based and technology-mediated methods.
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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 | 0.023 | 0.021 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.073 | 0.012 |
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".