Using Semistructured Telephone Interviews to Collect Qualitative Data From People With HIV Who Are Not in Medical Care: Implementation Study
Bibliographic record
Abstract
BACKGROUND: The Medical Monitoring Qualitative (MMP-Qual) Project was designed to collect qualitative data from people with HIV not engaged in medical care that would complement quantitative data collected by the Medical Monitoring Project (MMP)-a national surveillance system-and inform the MMP's recruitment and data collection methods. OBJECTIVE: Our objectives were to describe the methodology of this project, reflect on the challenges and lessons learned from conducting qualitative telephone interviews at a national level, and describe how we used and plan to use the qualitative data to evaluate our recruitment procedures and quantitative data collection instrument as well as knowledge of HIV care engagement. METHODS: We used stratified purposive sampling to identify and recruit participants who had participated in the structured MMP interview into the MMP-Qual Project. To be eligible, participants must have had an HIV diagnosis, be aged ≥18 years, have lived in an MMP jurisdiction, and have not been engaged in HIV medical care. From August 1, 2018, to May 31, 2019, we conducted semistructured telephone interviews with 36 people with HIV across the United States about several topics (eg, facilitators and barriers to care and experience with surveys). Four trained interviewers conducted semistructured 60-minute telephone interviews with 36 participants. Data collection lasted from August 1, 2018, to May 31, 2019. RESULTS: From 2018 to 2019, 113 people were eligible to participate in the MMP-Qual Project. Of the people recruited, 28% (22/79) refused to participate. Of those who agreed to participate, 63% (36/57) were interviewed, and 37% (21/57) were no-shows. Of the 34 participants for whom we had complete data, 15 (44%) were aged ≥50 years, 26 (76%) identified as male, 22 (65%) were Black or African American, and 12 (35%) lived in the Southern United States. CONCLUSIONS: We learned that it is possible to obtain rich qualitative data from people with HIV who are not in care via telephone interviews and that this mode might be conducive to talking about sensitive topics. We also learned the importance of flexibility, communication, and coordination because we relied on health department staff to perform recruitment and had difficulty implementing our original sampling strategy. We hope that other projects will learn from our experience conducting qualitative telephone interviews with people with HIV on a national level. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/40041.
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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 | 0.129 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".