Family-friendly labor policies and child supervision: Evidence from the Gambia, Mauritania, and São Tomé and Príncipe
Bibliographic record
Abstract
Background In many low- and middle-income countries (LMICs), children under five years old are frequently left home alone without adult supervision, exposing them to various risks. Family-friendly labor policies have the potential to improve parental care, but evidence of their effectiveness in LMICs remains scarce. Objective This study examines the association between labor market policies and the prevalence of unsupervised children in The Gambia, Mauritania, and São Tomé and Príncipe. Participants The study included a total of 42,399 children under five years of age, from two waves of the Multiple Indicator Cluster Surveys (MICS) conducted in The Gambia, Mauritania, and São Tomé and Príncipe. The surveys employed similar sampling strategies and survey weights, both of which was approved by UNICEF to ensure methodological rigor and representativeness. Methods A mixed-method approach was used to provide a comprehensive analysis of policy development and its impact on child adequate supervision. Using this method, we (a) tracked and verified policy development through key informant interviews, and (b) analyzed two waves of the Multiple Indicator Cluster Surveys (MICS). Findings Our findings indicate that while labor market policies alone do not significantly reduce unsupervised time (adjusted risk ratio = 0.59-1.74, 95% confidence interval), their integration into broader child welfare strategies could enhance child supervision. Conclusion This study highlights the need for robust policies to address child protection challenges in LMICs. While not sufficient alone, their effective implementation can improve child supervision as part of child welfare strategies.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".