Bibliographic record
Abstract
There is increasing evidence that digital technologies such as mobile phones have the potential to shape some of the United Nations (UN) Sustainable Development Goals (SDGs) such as health, education, and nutrition, even among the most resource-deprived countries and communities in the world. Nonetheless, little research has focused on the intergenerational implications of digital technologies for infant health and wellbeing. This study leverages Demographic and Health Survey (DHS) data from 29 low- and middle-income countries (LMICs) to explore associations between mothers' ownership of mobile phones and their children's health at birth, as measured by birth weight and low birth weight (LBW), i.e., weight lower than 2,500 grams. Infants born to women owning mobile phones fare consistently better in terms of birth weight, even after accounting for potential socioeconomic confounders and other sources of media or information in the household. Partly, mechanisms are consistent with the idea of broader knowledge and access to healthcare services, as associations are mediated by a higher number of antenatal visits, higher likelihood of having a birth assisted by a health professional, and by the extent to which mothers hear about family planning by text message. Associations are strongest among low-educated mothers. Also, associations are stronger in countries where infant health is poorer yet mobile-phone diffusion is higher, highlighting the comparatively higher potential of the diffusion of mobile phones for global development in poorest contexts. Our findings may be of interest to scholars and policymakers concerned with identifying relatively cheap policy levers to promote global health and wellbeing in disadvantaged contexts, particularly among women.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".