How and Where Do We Ask Sensitive Questions: Self-reporting of STI-associated Symptoms Among the Iranian General Population
Bibliographic record
Abstract
BACKGROUND: Reliable population-based data on sexually transmitted infections (STI) are limited in Iran and self-reporting remains the main source of indirect estimation of STI-associated symptoms in the country. However, where and how the questions are asked could influence the rate of self-reporting. In the present study, we aimed to assess what questionnaire delivery method (ie, face-to-face interview [FTFI], self-administered questionnaire [SAQ], or audio self-administered questionnaire [Audio-SAQ]) and setting (ie, street, household or hair salon) leads to more reliable estimates for the prevalence of self-reported STI-associated symptoms. METHODS: This cross-sectional study was conducted in winter 2014 on a gender-balanced (50.0% men) sample of 288 individuals aged 18-59 years old in Kerman, Iran. Respondents were recruited in (a) crowded public places and streets, (b) their households, and (c) hair salons. Data was collected on history of current and 6-month (ie, past 6 months) STI-associated symptoms. Three different methods including FTFI, SAQ and or Audio-SAQ were applied randomly in households and non-randomly in streets and hair salons to collect data among the respondents. Generalized estimating equation (GEE) was used to compare the settings and methods separately. RESULTS: A total of 2.8% of men and 9.4% of women self-reported at least one STI-associated symptom. Respondents were significantly more likely to report STI-associated symptoms when completing questionnaires on the street compared to their household (P = .0001). While women were less likely to report symptoms in FTFI compared to SAQ (P = .036), no significant differences were found between men's responses across different methods (P = .064). CONCLUSION: Further research is needed to evaluate the effect of different combinations of methods and settings to find the optimal way to collect data on STI-associated symptoms.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".