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Record W4205508915 · doi:10.21009/jpud.142.13

Understanding Parental Health Literacy for Clean and Healthy Behavior in Early Childhood During the Covid-19 Pandemic

2020· article· en· W4205508915 on OpenAlexaboutno aff
Safuri Musa, Sri Nurhayati

Bibliographic record

VenueJPUD - Jurnal Pendidikan Usia Dini · 2020
Typearticle
Languageen
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsnot available
Fundersnot available
KeywordsRespondentPandemicHealth literacyPsychologyLiteracyClean waterPerceptionDevelopmental psychologyCoronavirus disease 2019 (COVID-19)Health educationEarly childhoodMedicineEnvironmental healthPublic healthNursingHealth carePedagogyPolitical scienceDisease

Abstract

fetched live from OpenAlex

In the COVID-19 pandemic scenario, parents need to be familiar with health literacy by applying clean and healthy living habits to their family members, especially those with early childhood. This study aims to explain parents' perceptions of health literacy for a clean and healthy behavior in their children during the COVID-19 pandemic. The method used in this study is a cross-sectional study involving 22 men and 62 female respondents. Respondent requirements were used in data analysis to determine parents' perceptions of health literacy and the efforts they have made to practice clean and healthy lifestyle in their children. The research findings show that knowing the health awareness of parents has an impact on a child's balanced lifestyle. Based on six measures of clean and healthy behavior for children, three indicators were determined in the category of discipline and high discipline: using clean water, using the toilet, and doing physical activity. The act of washing children's hands with soap indicators has a high discipline score and the use of masks in children has low discipline. If the use of masks is not disciplined by parents, exposure to COVID-19 in early childhood can be disrupted.
 Keywords: Early Childhood, Parental health literacy, Clean and healthy behaviors
 References:
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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)0.001
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 score0.036
Threshold uncertainty score0.691

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.153
GPT teacher head0.387
Teacher spread0.234 · 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".

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Citations5
Published2020
Admission routes1
Has abstractyes

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