A systematic review and meta-analysis of quantitative interviewing tools to investigate self-reported HIV and STI associated behaviours in low- and middle-income countries
Bibliographic record
Abstract
OBJECTIVE: Studies identifying risks and evaluating interventions for human immunodeficiency virus (HIV) and other sexually transmitted infections often rely on self-reported measures of sensitive behaviours. Such self-reports can be subject to social desirability bias. Concerns over the accuracy of these measures have prompted efforts to improve the level of privacy and anonymity of the interview setting. This study aims to determine whether such novel tools minimize misreporting of sensitive information. METHODS: Systematic review and meta-analysis of studies in low- and middle-income countries comparing traditional face-to-face interview (FTFI) with innovative tools for reporting HIV risk behaviour. Crude odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Cochran's chi-squared test of heterogeneity was performed to explore differences between estimates. Pooled estimates were determined by gender, region, education, setting and question time frame using a random effects model. RESULTS: We found and included 15 data sets in the meta-analysis. Most studies compared audio computer-assisted self interview (ACASI) with FTFI. There was significant heterogeneity across studies for three outcomes of interest: 'ever had sex' (I(2) = 93.4%, P < 0.001), non-condom use (I(2) = 89.3%, P < 0.001), and number of partners (I(2) = 75.3%, P < 0.001). For the fourth outcome, 'forced sex', there was homogenous increased reporting by non-FTFI methods (OR 1.47; 95% CI 1.11-1.94). Overall, non-FTFI methods were not consistently associated with a significant increase in the reporting of all outcomes. However, there was increased reporting associated with non-FTFI with region (Asia), setting (urban), education (>60% had secondary education) and a shorter question time frame. CONCLUSION: Contrary to expectation, differences between FTFI and non-interviewer-administered interview methods for the reported sensitive behaviour investigated were not uniform. However, we observed trends and variations in the level of reporting according to the outcome, study and population characteristics. FTFI may not always be inferior to innovative interview tools depending on the sensitivity of the question as well as the population assessed.
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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 | 0.032 | 0.090 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.040 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".