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Record W4318479297 · doi:10.5472/marumj.1244642

Anger and aggression in children aged 6-12 in lockdowns during the COVID-19 pandemic in Turkey

2023· article· en· W4318479297 on OpenAlexaboutno aff
Hilal KURT SEZER, Nilay Bektaş Akpınar, Merve Aşkın Ceran, Gözdenur TANRIKULU

Bibliographic record

VenueMarmara medical journal · 2023
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsAngerPandemicAggressionCoronavirus disease 2019 (COVID-19)MedicineSocial isolationQuarter (Canadian coin)2019-20 coronavirus outbreakDemographyClinical psychologyPsychiatryDiseaseInfectious disease (medical specialty)VirologyGeography

Abstract

fetched live from OpenAlex

Objective: This study investigated the relationship between parents’ emotional states and the anger and aggression levels of their children between the ages of 6-12 who were in social isolation during the coronavirus disease 2019 (COVID-19) pandemic in Turkey. Materials and Methods: The study adopted a cross-sectional and descriptive design. The sample consisted of 261 parents. Data were collected online during lockdowns between May and September 2020 due to the COVID-19 pandemic. Results: More than half of the participants reported that they were unhappy (53.2%), sad (63.2%), and worried about the future due to the COVID-19 pandemic (70.8%). Less than a quarter of the participants were angry (17.6%). Children living in districts had significantly higher verbal aggression scores than those living in city centers (p

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)0.000
metaresearch head score (Gemma)0.001
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 score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.056
GPT teacher head0.418
Teacher spread0.361 · 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
Published2023
Admission routes1
Has abstractyes

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