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Record W4294934378 · doi:10.1093/jlb/lsac023

Ethicolegal considerations of screening for brain injury in women who have experienced intimate partner violence

2022· article· en· W4294934378 on OpenAlexaff
Quinn Boyle, Judy Illes, Deana Simonetto, Paul van Donkelaar

Bibliographic record

VenueJournal of Law and the Biosciences · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsNeuroDevNetUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsDomestic violenceDisadvantagedVulnerability (computing)Context (archaeology)Economic JusticeFocus groupVignettePsychologyMedicinePoison controlSuicide preventionCriminologySocial psychologyPolitical scienceMedical emergencyComputer securityBusinessLaw

Abstract

fetched live from OpenAlex

Abstract The vast majority of women who experience physical intimate partner violence (IPV) will likely suffer a brain injury (BI) as a result of the abuse. Accurate screening of IPV–BI can ensure survivors have access to appropriate health care and other supports, but screening results may also impact them receiving fair and equitable treatment in the legal system, and the justice they deserve. We used semi-structured interviews, combined with a contrastive vignette that described a realistic but hypothetical scenario involving IPV with or without BI, to explore the impact of BI on parenting disputes. Participants were lawyers (n = 12) whose focus is family law. Results highlight the potential adverse consequences of a positive BI screen that are influenced by the legal responsibility of counsel, the legal aid status of the woman, ongoing family dynamics, and the expectations of society while the focus on the best interests of the child is retained. Taken together, the findings reflect the legal vulnerability of women in decision-making about their capacity to parent after a BI. We conclude with recommendations for the future of IPV–BI screening aimed at mitigating risk and equipping women to navigate a legal system that has disadvantaged them, both historically and in the current context.

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)0.006
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.478
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.043
GPT teacher head0.365
Teacher spread0.322 · 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 designTheoretical or conceptual
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

Citations10
Published2022
Admission routes1
Has abstractyes

Explore more

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