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Record W3184291853 · doi:10.1038/s41598-021-95075-7

Biological and environmental influences on motor coordination in Peruvian children and adolescents

2021· article· en· W3184291853 on OpenAlexaff
Sara Pereira, Alcibíades Bustamante, Carla Santos, Donald Hedeker, Go Tani, Rui Garganta, Olga Vasconcelos, Adam Baxter‐Jones, Peter T. Katzmarzyk, José Maia

Bibliographic record

VenueScientific Reports · 2021
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsUniversity of Saskatchewan
FundersNational Institute of Environmental Health Sciences
KeywordsOverweightContext (archaeology)Gross motor skillLogistic regressionDemographyPsychological interventionObesityAltitude (triangle)Biological ageMultilevel modelMedicineEnvironmental healthGerontologyPsychologyGeographyMotor skillDevelopmental psychologyEndocrinology

Abstract

fetched live from OpenAlex

This study investigated the associations between biological and environmental factors and gross-motor coordination (GMC) in Peruvian children and adolescents. The sample comprised 7401 boys and girls, aged 6-14 years, recruited from three geographical regions: sea-level, Amazon and high-altitude. Biological variables included age, sex, height, BMI, physical fitness, stunting, and maturational status. Environmental influences included geographical region and school characteristics. Gross-motor coordination was tested with the Körperkoordinationstest für Kinder and the data analyzed by multilevel logistic regression. Results showed a high prevalence of below normal GMC scores. Sex, age, geographical area, biological maturation, BMI (normal versus overweight/obesity), and stunting were all significant predictors of GMC. There was also an interaction between age, sex, and geographical area indicating that older girls who lived at sea-level and high-altitude were more likely to display below normal GMC scores. The school context was less important in predicting GMC problems than the interplay between biological characteristics and geographical region. These results suggest that early identification, as well as educational and pediatric care interventions, are of importance in reducing below normal GMC among Peruvian children and adolescents.

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 score0.023
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

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

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 head0.010
GPT teacher head0.244
Teacher spread0.233 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

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