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Record W4225678478 · doi:10.1177/08862605221081934

Potential Reporters of Suspected Child Maltreatment are Sensitive to the Amount of Evidence and the Potential Consequences of Reporting

2022· article· en· W4225678478 on OpenAlexafffund
Heather L. Price, Andre Kehn

Bibliographic record

VenueJournal of Interpersonal Violence · 2022
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsThompson Rivers University
FundersSocial Sciences and Humanities Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsSalience (neuroscience)NeglectPsychologyChild abuseInjury preventionPoison controlSuicide preventionContext (archaeology)Human factors and ergonomicsOccupational safety and healthChild neglectSalientDevelopmental psychologyClinical psychologyMedicinePsychiatryMedical emergencyCognitive psychology

Abstract

fetched live from OpenAlex

= 368) and two experiments (Exp. 1 N = 444; Exp. 2 N =416), undergraduate students and online community participants reported their anticipated actions and beliefs when confronted with evidence of child maltreatment. Participants reviewed case dossiers built from real-world child neglect cases in which increasing levels of evidence were presented and the consequences of reporting, or not reporting, the maltreatment were made salient to the adult or child. The experiments revealed a clear difficulty in deciding whether or not to report suspected maltreatment. Highlighting the impact on either the child or the adult by describing potential consequences moved participants either closer to (child-salient) or farther from (adult-salient) a formal report. Participants were also sensitive to the amount of evidence to support a suspicion of abuse, which influenced the likelihood of a formal report. This work suggests that increasing the salience of maltreatment consequences to child victims may increase the likelihood that suspected maltreatment will be reported.

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)0.002
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 score0.456
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.032
GPT teacher head0.308
Teacher spread0.276 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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