An Adapted Cognitive Behavioral Stress and Self-management Intervention for Sexual Minority Men Living With HIV and Cancer Using the SmartManage eHealth Platform: Protocol and Study Design
Bibliographic record
Abstract
BACKGROUND: Sexual minority men are disproportionately affected by HIV. Medical advances in HIV treatment have extended life expectancy, and as this group ages, medical and psychological challenges become more prominent. Older people with HIV experience a higher incidence of cancer and other comorbidities; these burdens along with sexual minority stress can strain coping resources and diminish health-related quality of life. Interventions such as cognitive behavioral stress and self-management (CBSM) can mitigate some of this burden; however, no manualized, eHealth-based interventions have focused on the unique needs of sexual minority men living with HIV and cancer. OBJECTIVE: This study aims to refine and finalize a web-based, CBSM-based intervention to meet the unique needs of this population, including sexual health, comanagement of 2 chronic conditions, and coping with sexual minority stress. METHODS: This mixed methods study used a previously completed qualitative phase (n=6) to inform the development of a web-based platform and intervention called SmartManage. The pilot phase study (n=50) involved randomization (1:1) into either 10 sessions of adapted CBSM or an attention control health promotion. Both conditions used the SmartManage platform, a web-based eHealth program designed to deliver CBSM and health promotion content and host live groups. Feasibility and acceptability (eg, rates of participant engagement and retention) were the primary outcomes. RESULTS: Participant-related activities are expected to be completed by November 2022, and results are expected to be submitted for publication by February 2023. CONCLUSIONS: We hypothesize that participants would find the intervention acceptable (compared with engagement and retention rates observed in similar CBSM studies). We also hypothesize that participants receiving the SmartManage intervention would have reduced symptom burden and improved health-related quality of life before and after treatment compared with those who do not. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/37822.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".