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Record W4312716727 · doi:10.12974/2313-1047.2022.09.3

The Effect of COVID on Child Maltreatment: A Review

2022· review· en· W4312716727 on OpenAlexaff
Ami Rokach, Sybil Chan

Bibliographic record

VenueJournal of Psychology and Psychotherapy Research · 2022
Typereview
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsYork University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Livelihood2019-20 coronavirus outbreakPsychologyPeriod (music)Quality (philosophy)Developmental psychologyCriminologyMedicineHistoryVirology

Abstract

fetched live from OpenAlex

This article addresses child maltreatment during the period where COVID-19 entered our lives in 2020. Repeated lockdowns kept children at home, away from school, from their support systems, and from their daily routines. Parents have also been plagued by the economic challenges associated with remote living. This not only places additional stress on the quality of their livelihoods but also, renders their caregiving duties as exceedingly onerous. This article explores the reasons that ACEs increased during that time, and highlights what can parents, teachers, and the educational system do about it.

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)0.011
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.907
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0080.000

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 head0.155
GPT teacher head0.535
Teacher spread0.380 · 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 designNot applicable
Domainnot available
GenreReview

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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