The Relationship History Calendar: Improving the Scope and Quality of Data on Youth Sexual Behavior
Bibliographic record
Abstract
Most survey data on sexual activities are obtained via face-to-face interviews, which are prone to misreporting of socially unacceptable behaviors. Demographers have developed various private response methods to minimize social desirability bias and improve the quality of reporting; however, these methods often limit the complexity of information collected. We designed a life history calendar-the Relationship History Calendar (RHC)-to increase the scope of data collected on sexual relationships and behavior while enhancing their quality. The RHC records detailed, 10-year retrospective information on sexual relationship histories. The structure and interview procedure draw on qualitative techniques, which could reduce social desirability bias. We compare the quality of data collected with the RHC with a standard face-to-face survey instrument through a field experiment conducted among 1,275 youth in Kisumu, Kenya. The results suggest that the RHC reduces social desirability bias and improves reporting on multiple measures, including higher rates of abstinence among males and multiple recent sexual partnerships among females. The RHC fosters higher levels of rapport and respondent enjoyment, which appear to be the mechanisms through which social desirability bias is minimized. The RHC is an excellent alternative to private response methods and could potentially be adapted for large-scale surveys.
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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 | 0.052 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".