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Record W2621034465 · doi:10.1136/bmjpo-2017-000001

Child health: what should be done?

2017· editorial· en· W2621034465 on OpenAlexaboutno aff
Imti Choonara

Bibliographic record

VenueBMJ Paediatrics Open · 2017
Typeeditorial
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsnot available
Fundersnot available
KeywordsChild mortalityMillennium Development GoalsPovertyMortality rateMedicineInfant mortalityEnvironmental healthSocioeconomic statusEconomic growthDemographyPopulationEconomics

Abstract

fetched live from OpenAlex

Over 17 000 children under the age of 5 years die every day.1 The majority of these children die in either sub-Saharan Africa or South Asia. Most of these children die from preventable causes, including gastroenteritis, pneumonia and malaria. Progress has been made in reducing child mortality with a 50% reduction in under five mortality between 1990 and 2013.1 This was however less than the two-thirds reduction target in the Millennium Development Goals. Although the majority of deaths occur in low-income and middle-income countries, there are wide disparities in child mortality between high-income countries. Child mortality rates in the UK are higher than in many other European countries.2 Similarly, the USA has higher child mortality rates than neighbouring Canada and Cuba.1 Additionally within countries, there are wide disparities in child mortality. The poorest, most deprived sections of the community have the highest child mortality rates. The new United Nations Sustainable Development Goals (SDGs) include the reduction of poverty and inequalities as they recognise the link between socioeconomic factors and health. The evidence base for ensuring children are healthy is quite extensive. Politicians and governments are aware that the following are key contributors to ensuring that children are healthy. They are all included within the SDGs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.088
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0060.000
Scholarly communication0.0010.001
Open science0.0050.004
Research integrity0.0030.009
Insufficient payload (model declined to judge)0.0010.002

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 head0.143
GPT teacher head0.500
Teacher spread0.357 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations0
Published2017
Admission routes1
Has abstractyes

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