The Increasing Prevalence of Children Home Alone in Ghana: The Importance of Considering Regional Inequalities
Bibliographic record
Abstract
Research from industrialized settings has linked inadequate child supervision with various negative consequences. Nevertheless, empirical research in lower- and middle-income countries about correlates of inadequate child supervision has been scarce. The few studies that exist tended to focus on individual- and household-level factors, and reported associations that are not significant or in mixed directions depending on the context. Structural factors are left underexplored, but taking a more macro-level lens in settings with high regional disparities can hold the key to explaining increases in prevalence of inadequate child supervision. Exploring the evolution over time of child supervision practices can also enrich this explanation. We use data from two rounds of Ghana Multiple Indicator Cluster Surveys to examine factors associated with children left home alone, and employ regional analysis using strata-level mixed effects. We found that in Ghana, the prevalence of children left home alone without adult supervision increased by 8.5% between 2011 and 2018 - an increase of more than 500,000 children over seven years. Statistical analyses suggest that variation between regions likely are associated with the growth of inadequate child supervision in this country. Future research should pay closer attention to how structural conditions, proxied by regions, can serve as either barriers or facilitators to adequate child supervision practices, helping shed light on residual variance unexplained by individual- and household-level factors. Supplementary Information: The online version contains supplementary material available at 10.1007/s12187-023-10038-w.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".