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Record W2006238554 · doi:10.1080/17450128.2015.1034799

Migration as a risk factor for school dropout amongst children made vulnerable by HIV/AIDS: a prospective study in eastern Zimbabwe

2015· article· en· W2006238554 on OpenAlexfundno aff
Erica L. Pufall, Constance Nyamukapa, Laura Robertson, Paradzai George Mushore, Albert Takaruza, Simon Gregson

Bibliographic record

VenueVulnerable Children and Youth Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsnot available
FundersEconomic and Social Research CouncilCanadian Institutes of Health ResearchWellcome TrustWellcome
KeywordsDropout (neural networks)MedicineHuman immunodeficiency virus (HIV)Risk factorEnvironmental healthProspective cohort studyFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

Orphans and other children made vulnerable by HIV in sub-Saharan Africa are at increased risk of moving household and of dropping out of school. However, the relationship between child migration and school enrolment has not been established. Multivariable regression models and prospective data from a cohort of children in Manicaland, Zimbabwe, were used to investigate the effect of migration on school enrolment. Children who had moved household were at increased risk of dropping out of school after adjusting for orphan status, relationship to primary caregiver, and household wealth. Interventions are needed to ensure that children who migrate are re-enrolled in school.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.023
GPT teacher head0.300
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2015
Admission routes1
Has abstractyes

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