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Record W2034734329 · doi:10.1186/1471-2458-13-1049

A comparative analysis of early child health and development services and outcomes in countries with different redistributive policies

2013· article· en· W2034734329 on OpenAlexaff
Meta van den Heuvel, Jessica Hopkins, Anne Biscaro, Cinntha Srikanthan, Andrea Feller, Sven Bremberg, Nienke Verkuijl, Boudien Flapper, Elizabeth Ford-Jones, Robin Williams

Bibliographic record

VenueBMC Public Health · 2013
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsMinistry of Health and Long Term CareUniversity of TorontoSickKids FoundationRegional Municipality of NiagaraHospital for Sick ChildrenMcMaster University
Fundersnot available
KeywordsMedicinePublic healthHealth policyEconomic growthSocial policyEarly childhoodSocial determinants of healthChild developmentHealth careBiostatisticsSocial WelfarePolitical scienceEconomicsPsychologyDevelopmental psychologyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: The social environment is a fundamental determinant of early child development and, in turn, early child development is a determinant of health, well-being, and learning skills across the life course. Redistributive policies aimed at reducing social inequalities, such as a welfare state and labour market policies, have shown a positive association with selected health indicators. In this study, we investigated the influence of redistributive policies specifically on the social environment of early child development in five countries with different political traditions. The objective of this analysis was to highlight similarities and differences in social and health services between the countries and their associations with other health outcomes that can inform better global early child development policies and improve early child health and development. METHODS: Four social determinants of early child development were selected to provide a cross-section of key time periods in a child's life from prenatal to kindergarten. They included: 1) prenatal care, 2) maternal leave, 3) child health care, and 4) child care and early childhood education. We searched international databases and reports (e.g. Organization for Economic Cooperation and Development, World Bank, and UNICEF) to obtain information about early child development policies, services and outcomes. RESULTS: Although a comparative analysis cannot claim causation, our analysis suggests that redistributive policies aimed at reducing social inequalities are associated with a positive influence on the social determinants of early child development. Generous redistributive policies are associated with a higher maternal leave allowance and pay and more preventive child healthcare visits. A decreasing trend in infant mortality, low birth weight rate, and under five mortality rate were observed with an increase in redistributive policies. No clear influence of redistributive policies was observed on breastfeeding and immunization rates. In the analysis of child care and early education, the lack of uniform measures of early child development outcomes was apparent. CONCLUSIONS: This paper provides further support for an association between redistributive policies and early child health and development outcomes, along with the organization of early child health and development services.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.024
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.008
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.319
Teacher spread0.285 · 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

Citations23
Published2013
Admission routes1
Has abstractyes

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