Association between exposure to social media and knowledge of sexual and reproductive health among adolescent girls: evidence from the UDAYA survey in Bihar and Uttar Pradesh, India
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Poor sexual and reproductive health (SRH) outcomes amongst adolescent girls in India have been associated with inadequate knowledge of SRH. Evidence suggests that social media can promote health-seeking behaviors. Our objective in this study was to determine the association between exposure to social media and SRH knowledge among adolescent girls in Bihar and Uttar Pradesh, India. METHODS: A cross-sectional study was conducted with 10,425 adolescent girls from the UDAYA survey (wave-2, 2018-19). Girls' exposure to social media was the key predictor, and SRH knowledge of sexual intercourse and pregnancy, contraceptive methods, and HIV/AIDS were outcomes of interest. Multivariable logistic regression models were performed to assess the association between exposure to social media and knowledge of SRH among adolescent girls. RESULTS: Of the study participants (n = 10,425), 28.0% (n = 3,160) had exposure to social media. Overall, 8.7%, 11.4%, and 6.6% of respondents had sufficient knowledge of sexual intercourse and pregnancy, contraceptive methods, and HIV/AIDS, respectively. Exposure to social media was associated with increased odds of knowledge of sexual intercourse and pregnancy (Odds ratio [OR]: 1.38; 95% confidence interval [CI]: 1.18, 1.61), contraceptive methods (OR: 1.46; 95% CI: 1.27, 1.67), and HIV/AIDS (OR: 2.18; 95% CI: 1.84, 2.58). CONCLUSIONS: Our study shows the potency of exposure to social media in influencing SRH knowledge, which exclusively benefits female adolescents who are educated, residing in urban areas, and from wealthier families. Digital media-focused interventions inclusive of socio-cultural contexts (e.g., strategic investment in education and creating economic opportunities) are crucial to optimize social media's impact on SRH knowledge enhancements.
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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 | 0.022 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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