The health status and related interventions for children left behind due to parental migration in the Philippines: A scoping review
Bibliographic record
Abstract
Overseas Filipino Workers are hailed as modern-day heroes who enable their families to climb the socioeconomic ladder. Despite their financial contribution, labour migration often separates children from their parents during their most formative years of growth, threatening healthy development. Using the Joanna Briggs Institute's framework, a scoping review was conducted to identify the health outcomes of left behind children in the Philippines and health-related interventions. In total, 4440 records were collected from peer-reviewed articles and grey literature and 50 records were eligible for inclusion. The findings indicated that left behind children experience a vast range of poor physical (general health, hygiene, illness, and nutrition) and mental (behavioural, cognitive, and emotional) health outcomes. A total of 48 interventions were identified in 13 out of 17 geographic regions. Despite this geographic coverage, the evidence-based literature was limited with regard to whether these interventions have been effective. Additional research is needed to better understand children's health, evaluate existing interventions, and develop multisectoral programming. Funding: This review was supported by the Center for Global Health Equity, NYU Shanghai. No funding agencies were involved in the data collection, data analysis, and writing of this paper.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".