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Record W83470573 · doi:10.1093/pch/13.10.857

Social paediatrics and early child development – the practical enhancements: Part 2

2008· article· en· W83470573 on OpenAlexaff
Jane T. Bertrand, Robin Williams, Lee Ford-Jones

Bibliographic record

VenuePaediatrics & Child Health · 2008
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsSickKids FoundationUniversity of TorontoMcMaster UniversityHospital for Sick ChildrenRegional Municipality of NiagaraGeorge Brown College
Fundersnot available
KeywordsPsychological interventionReading (process)Early childhoodChild developmentMedicineDevelopmental psychologyChild healthPopulationDevelopmentally Appropriate PracticePsychologyMedical educationPediatricsNursingEarly childhood educationEnvironmental healthPolitical science

Abstract

fetched live from OpenAlex

Parents have central and critical influence in the health, learning and development of their young children. The physician plays a key role in supporting this role of parents, and there are practical health interventions that practitioners can promote in everyday practice that are coherent with the population-based evidence related to childhood outcomes. Four child development enhancers are recognized - emotional awareness, reading books, appropriate discipline and preschool programs including appropriate play opportunities. The child's physician can give clear messages about why each enhancer is important and what parents can do to use them to create nurturing environments for their children. The present article provides the evidence for these interventions and a series of coordinated physician activities that will enhance the early learning opportunities of the first few years of life, for improved trajectories for health and well being.

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)0.003
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: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0220.002

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 head0.057
GPT teacher head0.379
Teacher spread0.322 · 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

Citations14
Published2008
Admission routes1
Has abstractyes

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