DYnamics of Contraception in Eswatini (DYCE): protocol for a high-frequency, longitudinal cell phone survey
Bibliographic record
Abstract
INTRODUCTION: By 2030, an estimated 42% of the world's adolescent girls and young women (AGYW) will live in sub-Saharan Africa (SSA), where a quarter of AGYW pregnancies are undesired and AGYW represent 75% of new HIV infections. Most AGYW in SSA use short-acting contraceptive methods, including many who rely solely on condoms, which results in variable patterns of protection against undesired pregnancies and HIV. Dynamics of contraceptive use and HIV prevention efforts are poorly understood but the DYnamics of Contraception in Eswatini study will contribute to the understanding of the complex relationship between sexual behaviours, risk perception and HIV and pregnancy prevention. METHODS AND ANALYSIS: All AGYW 18-24 years who participated in a 2021 nationally representative, population-based (face-to-face) HIV survey, consented to future research and provided a mobile phone number will be contacted for enrolment. Consenting AGYW will complete a baseline questionnaire and be surveyed biweekly for 2 years. The three objectives of the study are to (1) investigate how changing individual context (schooling, income, residential mobility) impact contraceptive use; (2) examine the relationship between pregnancy desires and contraceptive use and (3) test whether changes in a woman's perception of her HIV risk changes her probability of contraceptive use and HIV protective behaviours. Data collection will take place from March 2024 to March 2026 via computer-assisted telephone interviews. During recruitment (March-April 2024), two interviewers called 794 AGYW, of whom 326 completed the baseline questionnaire (41%) and 321 consented to biweekly follow-up (40% American Association for Public Opinion Research response rate #1). Analyses will mainly use hybrid within-between logistic regression models. ETHICS AND DISSEMINATION: This protocol was reviewed and approved by the Eswatini National Health Research Review Board in 2024 and Columbia University Medical Center Institutional Review Board in 2023. Findings from the study will inform the Eswatini Ministry of Health approaches to mitigating undesired pregnancies and HIV among AGYW. Second, few examples exist of high-frequency longitudinal data collection in SSA, and this study will contribute to the survey method knowledge. Finally, the rich dataset will available for secondary data analysis.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.015 |
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".