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Record W4312450826 · doi:10.35473/proheallth.v4i1.1809

Pengaruh Efektivitas Alat Permainan Puzzle Terhadap Perkembangan Motorik Halus Pada Anak Usia 4-5 Tahun Di Ypi Zaky River Valley Kabupaten Deli Serdang Tahun 2020

2022· article· en· W4312450826 on OpenAlexaff
Heny Rista, Juliana Munthe, Srilina Br Pinem, Lasria Simamora, Ade Srimulyani

Bibliographic record

VenuePro Health Jurnal Ilmiah Kesehatan · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Education
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsWilcoxon signed-rank testPsychologyChecklistBivariate analysisPopulationDemographyStatisticsMathematicsSociology

Abstract

fetched live from OpenAlex

Childhood is the most important period for children's development so it is called the Golden Age, usually at this time children aged 0-6 years who experience the fastest stages of physical and spiritual growth and development, both physically and mentally. This study aims to analyze the effect of the effectiveness of playing puzzles on fine motoric development in children aged 4-5 years in Zaky River Valley Kindergarten. Population is all elements research subjects of children who study at YPI Zaky River Valley as many as 20 students, the number of students from March to May 2020. This is an analytical survey with a cross sectional research design. Retrieval of data using a checklist sheet. The data were processed by univariate analysis using descriptive statistics and bivariate analysis using the Wilcoxon test. The results of the analysis for knowledge and attitudes with the Wilcoxon test showed that the value of p = 0.000 which means less than α = 0.005, then H0 is rejected and Ha is accepted. This means that there is an effect of the effectiveness of playing puzzles on the level of anxiety on fine motoric development in children aged 4-5 years at YPI Zaky River Valley Deli Serdang Regency in 2020.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.566
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0070.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.324
Teacher spread0.290 · 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.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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