Social networks and female reproductive choices in the developing world: a systematized review
Bibliographic record
Abstract
Continuing high global maternal mortality and morbidity rates in developing countries have resulted in an increasing push to improve reproductive health services for women. Seeking innovative ways for assessing how positive health knowledge and behaviors spread to this vulnerable population has increased the use of social network theories and analysis in health promotion research. Despite the increased research on social networks and health, no overarching review on social networks and maternal health literature in developing countries has been conducted. This paper attempts to synthesize this literature by identifying both published and unpublished studies in major databases on social networks and maternal and child health. This review examined a range of study types for inclusion, including experimental and non-experimental study designs including randomized controlled trials, non-randomized controlled trials, quasi-experimental, cohort studies, case control studies, longitudinal studies, and cross-sectional observational studies. Only those that occurred in developing countries were included in the review. Eighteen eligible articles were identified; these were published between 1997 and 2012. The findings indicated that the most common social network mechanisms studied within the literature were social learning and social influence. The main outcomes studied were contraceptive use and fertility decisions. Findings suggest the need for continuing research on social networks and maternal health, particularly through the examination of the range of social mechanisms through which networks may influence health behaviors and knowledge, and the analysis of a larger variety of reproductive outcomes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".