MétaCan
Menu
Back to cohort

Daily Life Activities of Children during the Pandemic

2021· article· en· W3196018067 on OpenAlexvenueno aff
Beyhan Özge Yersel, Lügen Ceren GÜNEŞ, Ender Durualp

Bibliographic record

VenueInternational Journal of Child Health and Nutrition · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePandemicSnowball samplingDuration (music)Reading (process)Sample (material)Developmental psychologyDescriptive researchGerontologyCoronavirus disease 2019 (COVID-19)PsychologyDisease

Abstract

fetched live from OpenAlex

The aim of this descriptive study was to examine the views of parents with children between the ages of 3-6 on their children's daily life activities during the pandemic. The study sample was composed of 65 parents, among whom 60 were mothers, and five were fathers, who were selected with the snowball method and who had children between the ages of 3-6 and voluntarily participated in the study. The data were collected through the General Information Form and the Family Interview Form, which were developed in line with expert opinions. The collected data were analyzed using percentage and frequency values. The findings suggested that, during the pandemic, the children's family relationships were positively affected; the duration of using technological tools increased; the children started to wash their hands more carefully; and duration of activities, such as drawing and chores, and plays increased. It was also found that the children mostly preferred piece assembly games; their physical movement needs were not fully satisfied; and there was no change in their health conditions, self-care skills, diet, sleep patterns, interactive book reading, and purposes of using technology. In line with the findings, parents, experts were given specific recommendations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.015
GPT teacher head0.308
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 source (direct Gemma or distilled Codex), 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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Child Health and NutritionSame topicChild Development and Digital TechnologyFrench-language works237,207