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Cross-cultural analysis of the motor development of Brazilian, Greek and Canadian infants assessed with the Alberta Infant Motor Scale

2013· article· en· W2097956240 on OpenAlexaboutno aff
Raquel Saccani, Nádia Cristina Valentini

Bibliographic record

VenueRevista Paulista de Pediatria · 2013
Typearticle
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsnot available
Fundersnot available
KeywordsPercentileMotor skillPopulationObservational studyDemographyDescriptive statisticsMedicinePediatricsPsychologyDevelopmental psychologyEnvironmental healthStatistics

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare the motor development of infants from three population samples (Brazil, Canada and Greece), to investigate differences in the percentile curves of motor development in these samples, and to investigate the prevalence of motor delays in Brazilian children. METHODS: Observational, descriptive and cross-sectional study with 795 Brazilian infants from zero to 18 months of age, assessed by the Alberta Infant Motor Scale (AIMS) at day care centers, nurseries, basic health units and at home. The Brazilian infants' motor scores were compared to the results of two population samples from Greece (424 infants) and Canada (2,400 infants). Descriptive statistics was used, with one-sample t-test and binomial tests, being significant p ≤ 0.05. RESULTS: 65.4% of Brazilian children showed typical motor development, although with lower mean scores. In the beginning of the second year of life, the differences in the motor development among Brazilian, Canadian and Greek infants were milder; at 15 months of age, the motor development became similar in the three groups. A non-linear motor development trend was observed. CONCLUSIONS: The lowest motor percentiles of the Brazilian sample emphasized the need for national norms in order to correctly categorize the infant motor development. The different ways of motor development may be a consequence of cultural differences in infant care.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.046
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.258
Teacher spread0.249 · 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 teacher head, 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

Citations41
Published2013
Admission routes1
Has abstractyes

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