Considerations for Conducting Web-Based Survey Research With People Living With Human Immunodeficiency Virus Using a Community-Based Participatory Approach
Bibliographic record
Abstract
BACKGROUND: Web or Internet-based surveys are increasingly popular in health survey research. However, the strengths and challenges of Web-based surveys with people living with human immunodeficiency virus (HIV) are unclear. OBJECTIVE: The aim of this article is to describe our experience piloting a cross-sectional, Web-based, self-administered survey with adults living with HIV using a community-based participatory research approach. METHODS: We piloted a Web-based survey that investigated disability and rehabilitation services use with a sample of adults living with HIV in Canada. Community organizations in five provinces emailed invitations to clients, followed by a thank you/reminder one week later. We obtained survey feedback in a structured phone interview with respondents. Participant responses were transcribed verbatim and analyzed using directed content analysis. RESULTS: Of 30 people living with HIV who accessed the survey link, 24/30 (80%) initiated and 16/30 (53%) completed the survey instrument. A total of 17 respondents participated in post-survey interviews. Participants described the survey instrument as comprehensive, suggesting content validity. The majority (13/17, 76%) felt instruction and item wording were clear and easy to understand, and found the software easy to navigate. Participants felt having a pop-up reminder directing them to missed items would be useful. CONCLUSIONS: Strengths of implementing the Web-based survey included: our community-based participatory approach, ease of software use, ability for respondents to complete the questionnaire on one's own time at one's own pace, opportunity to obtain geographic variation, and potential for respondent anonymity. Considerations for future survey implementation included: respondent burden and fatigue, the potentially sensitive nature of HIV Web-based research, data management and storage, challenges verifying informed consent, varying computer skills among respondents, and the burden on community organizations. Overall, results provide considerations for researchers conducting community-based participatory Web-based survey research with people living with HIV.
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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 | 0.560 | 0.554 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.017 | 0.015 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".