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Infant development and parents’ perceptions associated with use of the harris infant neuromotor test

2010· article· en· W1488949701 on OpenAlexafffund
Maria Vera Lúcia Moreira Leitão Cardoso, Polyana Candeia Maia, Larissa Paiva Silva, Grazielle Roberta Freitas da Silva, Virginia E. Hayes, Susan R. Harris

Bibliographic record

VenueRev Rene · 2010
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsCanadian Physiotherapy AssociationUniversity of Victoria
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoUniversity of Victoria
KeywordsTest (biology)PerceptionMedicinePortuguesePediatricsInfant developmentDescriptive statisticsPsychologyDemographyDevelopmental psychology

Abstract

fetched live from OpenAlex

This work aimed to identify the socio-economic, health, and educational profiles of parents, as well as their perceptions of their infants’ motor development in the first year of life. This descriptive study was carried out from November/2008 to February/2009. Participants in the study were 50 infants and 50 parents/guardians, in Fortaleza-Brazil. We used the Portuguese version of the Harris Infant Neuromotor Test (HINT) with infants ranging in age from 3 months to 11 months and 20 days. Twenty-six infants (52%) were boys. The mean age of the mothers was 24.5 years. Of the caregivers, 23 (46%) lived in stablemarital relationships, Sixteen (32%) had finished secondary education, and 27 (54%) of the families had incomes between R$ 464,72 (US$202.05) and R$ 929,44 (US$404.10). Participating caregivers were generally accurate in their own perceptions of their children’s development when compared to the numeric HINT scores assessed by nurses trained in infant development. DOI:https://doi.org/10.15253/2175-6783.2010011esp000014

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)0.006
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 score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.233
Teacher spread0.213 · 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

Citations1
Published2010
Admission routes2
Has abstractyes

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