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Record W2736233486 · doi:10.1097/jfn.0000000000000159

Mandatory Reporting of Intimate Partner Violence: An Ethical Dilemma for Forensic Nurses

2017· article· en· W2736233486 on OpenAlexaff
Rosalyn M. Walker

Bibliographic record

VenueJournal of Forensic Nursing · 2017
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsForensic nursingConfidentialityDomestic violenceMandateLaw enforcementMedical emergencyMedicineAutonomyMandatory reportingPoison controlSuicide preventionNursingPsychologyCriminologyLawPolitical science

Abstract

fetched live from OpenAlex

Nearly all states and provinces have laws mandating licensed healthcare professionals to report to law enforcement suspicions and allegations of the abuse of children, older adults, and disabled persons and all incidents of violence by a deadly weapon. However, a few states in the United States additionally mandate providers to report all injuries resultant from reported or suspected domestic/intimate partner violence. This can present a challenge to forensic nurses seeking to protect patient confidentiality and autonomy. This challenge becomes further compounded when a patient desiring to remain anonymous reports sexual assault by their partner, accompanied by bodily injury. This case report explores one such scenario that occurred in a rural Colorado Emergency Department, the issues this presents to forensic nurses, and possible responses.

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)0.040
metaresearch head score (Gemma)0.100
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.040
Threshold uncertainty score0.213

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.100
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.010
Scholarly communication0.0070.005
Open science0.0030.007
Research integrity0.0100.012
Insufficient payload (model declined to judge)0.0020.001

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 head0.055
GPT teacher head0.414
Teacher spread0.359 · 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 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

Citations14
Published2017
Admission routes1
Has abstractyes

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