Interviewer effects on the reporting of intimate partner violence in the 2015 Zimbabwe Demographic and Heath Survey
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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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".