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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".