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Record W2740023962 · doi:10.1177/0886260517723140

Qualitatively Understanding Mother Fault After Childhood Sexual Abuse

2017· article· en· W2740023962 on OpenAlexafffund
Jessica L. Zagrodney, Jorden A. Cummings

Bibliographic record

VenueJournal of Interpersonal Violence · 2017
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversity of Saskatchewan
FundersSaskatchewan Health Research Foundation
KeywordsSexual abusePoison controlSuicide preventionHuman factors and ergonomicsPsychologyInjury preventionOccupational safety and healthChild abuseMedical emergencyDevelopmental psychologyClinical psychologyMedicine

Abstract

fetched live from OpenAlex

Socially constructed images of motherhood suggest that a "good" mother is caring, nurturing, and selfless-the perfect maternal figure. When these standards are not met, mother blaming (i.e., assigning fault to mothers) occurs even in child sexual abuse (CSA) cases. We collected 312 open-ended responses in total from 108 community-based participants to understand contextual factors that increase and decrease in mother fault in a CSA-related vignette depicting the mother's partner as the perpetrator. Thematic analysis revealed five main themes. Three themes were associated with decreased blame: Lack of Overt Knowledge (i.e., the mother had no direct knowledge of the CSA and thus cannot be blamed), Physical Act (i.e., the mother was not the actual perpetrator; only the perpetrator is responsible for the CSA), and Trust (i.e., the mother should be able to trust her partner). Two themes were associated with increased blame: Covert Knowledge (i.e., the mother was expected to have covert, intuitive knowledge of the CSA) and Mistrust (i.e., the mother should have known better than to trust her partner). Faulting mothers for the CSA of their child may reduce reporting of, and help seeking for, CSA, due to fear of being blamed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.702
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.351
Teacher spread0.295 · 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 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

Citations6
Published2017
Admission routes2
Has abstractyes

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