Interviewer effects on the reporting of intimate partner violence in the 2015 Zimbabwe Demographic and Heath Survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Intimate partner violence is a global public health concern that is widely under-reported. Socio-demographic factors of the interviewer may contribute to a reluctance to report violence. The introduction of the fieldworker survey to the 2015 Zimbabwe Demographic and Health Survey provides the first opportunity to test associations between interviewer characteristics and the reporting of intimate partner violence in the largest source of IPV data on intimate partner violence available for low- and middle-income countries. Three separate, multilevel logistic regression models were used to examine associations between the reporting of physical, sexual and emotional intimate partner violence and interviewer characteristics (age, sex and marital status, as well as differences in these indicators between interviewer and respondent), language of the interview and the interviewer’s previous experience conducting the Demographic and Health Survey. Previous experience as a Demographic and Health Survey interviewer was associated with significantly lower odds (OR: 0.67) of reporting physical intimate partner violence. Researchers should consider using the fieldworker data set in future studies to control for potential interviewer error, account for the clustering of data by interviewer and increase the robustness of Demographic and Health Survey analyses. Understanding how interviewers may shape the reporting of intimate partner violence is a step towards accurately measuring its burden in low- and middle-income countries.
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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 | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 it