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Record W3194461462 · doi:10.1111/nin.12453

Nursing violent patients: Vulnerability and the limits of the duty to provide care

2021· article· en· W3194461462 on OpenAlexafffund
Jennifer Dunsford

Bibliographic record

VenueNursing Inquiry · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsUniversity of Manitoba
FundersDalhousie University
KeywordsVulnerability (computing)DutyHarmDuty of careNursingNursing careMedicinePsychologySocial psychologyLawPolitical scienceComputer security

Abstract

fetched live from OpenAlex

The duty to provide care is foundational to the nursing profession and the work of nurses. Unfortunately, violence against nurses at the hands of recipients of care is increasingly common. While employers, labor unions, and professional associations decry the phenomenon, the decision to withdraw care, even from someone who is violent or abusive, is never easy. The scant guidance that exists suggests that the duty to care continues until the risk of harm to the nurse is unreasonable, however, "reasonableness" remains undefined in the literature. In this paper, I suggest that reasonable risk, and the resulting strength of the duty to provide care in situations where violence is present, hinge on the vulnerability of both nurse and recipient of care. For the recipient, vulnerability increases with the level of dependency and incapacity. For the nurse, vulnerability is related to the risk and implications of injury. The complex interplay of contextual vulnerabilities determines whether the risk a nurse faces at the hands of a violent patient is reasonable or unreasonable. This examination will enhance our understanding of professional responsibilities and can help to clarify the strengths and limitations of the nurse's duty to care.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score0.714

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.038
GPT teacher head0.351
Teacher spread0.313 · 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 designQualitative
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

Citations12
Published2021
Admission routes2
Has abstractyes

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