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Record W4389365049 · doi:10.9745/ghsp-d-23-00181

Advocacy for Better Integration and Use of Child Health Indicators for Global Monitoring

2023· article· en· W4389365049 on OpenAlexaff
Kathleen Strong, Jennifer Requejo, Sk Masum Billah, Joanna Schellenberg, Melinda Munos, Marzia Lazzerini, Ambrose Agweyu, Cynthia Boschi-Pinto, Sayaka Horiuchi, Abdoulaye Maïga, Ralf Weigel, Zeina Jamaluddine, Maureen M. Black, Frances E. Aboud, Emma Sacks

Bibliographic record

VenueGlobal Health Science and Practice · 2023
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsMcGill University
FundersWorld Health OrganizationUnited States Agency for International Development
KeywordsData collectionNormativeCapacity buildingHealth indicatorChild healthResource allocationMonitoring and evaluationResource (disambiguation)Environmental healthBusinessComputer scienceMedicinePolitical scienceEconomic growthPopulationPediatricsSociologyEconomics

Abstract

fetched live from OpenAlex

Key Messages Standard validated indicators that have been recommended by normative agencies exist and should be used as appropriate for monitoring child health outcomes. Vertical child health monitoring and evaluation approaches can distort the prioritization of health issues at the country level and may skew national resource allocation. A focus on building country capacity to improve child health data collection and analysis can minimize the need for complex statistical methods to estimate national values.

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)0.179
metaresearch head score (Gemma)0.287
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.179
Threshold uncertainty score0.945

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1790.287
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0040.005
Science and technology studies0.0020.010
Scholarly communication0.0100.021
Open science0.0060.013
Research integrity0.0220.037
Insufficient payload (model declined to judge)0.0150.005

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 head0.051
GPT teacher head0.435
Teacher spread0.384 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations2
Published2023
Admission routes1
Has abstractyes

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