Bridging the Digital Divide Among Racial and Ethnic Minority Men Who Have Sex With Men to Reduce Substance Use and HIV Risk: Mixed Methods Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Ecological momentary assessment (EMA) is a promising data collection tool for mobile health interventions targeting episodic health behaviors. For substance-using men who have sex with men (SUMSM), EMA is becoming more widely utilized in efforts to characterize substance use and sexual risk factors for HIV transmission. However, recent literature demonstrates emerging concerns over compliance and lower EMA engagement and data concordance among racial and ethnic minority SUMSM. OBJECTIVE: This study aimed to provide a qualitative evaluation of the barriers and facilitators of EMA as a data collection tool among racial and ethnic minority SUMSM. METHODS: Between October and November 2017, 45 racial and ethnic minority SUMSM were recruited from a list of prior research participants at the San Francisco Department of Public Health to participate in daily EMA surveys on their substance use and sexual health behaviors for 1 week, followed by in-person focus groups (FGs). A total of 4 FGs explored the participants' experiences with the surveys, issues regarding privacy and confidentiality, and suggestions for improvement. Qualitative analysis was performed using content analysis. Descriptive statistics and Fisher exact tests were used to assess the associations between demographics or substance use behaviors and EMA completion. RESULTS: Overall, 93.9% (295/314) of all delivered surveys were initiated, and of those, 98.0% (289/295) were completed. Neither participant demographics, including race (P=.65) or age (P=.43), nor substance use behaviors, including the frequency of alcohol (P=.40) or methamphetamine (P=.91) use or any cocaine (P=.28), crack (P=.99), or polysubstance use (P=.24), were found to be associated with survey completion. Overall, participants were receptive to the text message-based EMA surveys. Facilitators included survey timing, user-friendly survey design, survey-stimulated self-reflection, coding of sensitive phrases, and other privacy benefits of a mobile survey. Barriers included an inability to correct texting errors and participants' perception of judgment or stigmatization related to questions about condomless sex. To improve EMA compliance and uptake, participants suggested adding response confirmations, clarifying survey language, and continuing to diversify the study audience. CONCLUSIONS: EMA appears to be feasible and acceptable among this sample of racial and ethnic minority SUMSM. Close attention to EMA study design and the development of nonjudgmental, contextualized questions regarding stigmatized health behaviors may be critical to further improve EMA compliance.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".