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Record W4393384693 · doi:10.26714/jk.13.1.2024.1-10

The Effectiveness of Emotion Validation Pop-Up Books on the Emotional Development of Preschool Children as a Control for Children’s Mental Health Emergencies

2024· article· en· W4393384693 on OpenAlexaboutno aff
Maria Ulfah Kurnia Dewi, Nuke Devi Indrawati, Praewathip Suthiraprasert

Bibliographic record

VenueJurnal Kebidanan · 2024
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthPsychologyEmotional developmentDevelopmental psychologyControl (management)Child developmentClinical psychologyPsychiatrySocial changeComputer sciencePolitical scienceArtificial intelligence

Abstract

fetched live from OpenAlex

One of the psychological problems experienced by children is emotional mental problems which can result in unhealthy emotional and mental disorders. The incidence of this disorder is around 3-10%, in the United States it is around 3-7% while in Germany, Canada and New Zealand it is around 5-10%. In Indonesia, there are still no definite figures regarding the incidence, even though this disorder occurs quite often. The Emotion Validation Pop-Up Book Research contributes to Preschool Children increasing their understanding of felt emotions through an attractive display. This research uses a quasi-experimental design. The number of samples used in the treatment group and control group was 30 students at Kindergarten N Pembina Kab. Kendal uses a simple random sampling technique. Statistical analysis uses independent T test and paired T test. The results showed that the research showed an independent T test (0.000<α) and paired T test (0.000<α). This study concluded that there were differences in the emotional development of preschool children between the control and treatment groups and there were differences before and after in the treatment group in the use of the Preschool Children’s Emotion Validation Pop-Up Book.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.933
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.019
GPT teacher head0.312
Teacher spread0.293 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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