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Record W92375317

Factors related to on-the-job abuse of nurses by patients.

2009· article· en· W92375317 on OpenAlexaffabout
Margot Shields, Kathryn Wilkins

Bibliographic record

VenuePubMed · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsStaffingVerbal abuseNursingMedicineHealth carePsychological abuseOccupational safety and healthWorkplace violencePsychologySuicide preventionPoison controlMedical emergencyChild abuse
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Numerous studies indicate that health care providers, particularly nurses, face a high risk of on-the-job abuse from patients. This article examines physical and emotional abuse from patients in nurses working in hospitals or long-term care facilities. DATA AND METHODS: Data are from the 2005 National Survey of the Work and Health of Nurses. Cross-tabulations were used to examine abuse in relation to personal characteristics of the nurse, job characteristics, and workplace climate factors. Multiple logistic regression modeling was used to examine abuse in relation to staffing and resource adequacy and relations among colleagues, controlling for personal and job characteristics. RESULTS: In 2005, 34% of Canadian nurses providing direct care in hospitals or long-term care facilities reported physical assault by a patient in the previous year; 47% reported emotional abuse. Abuse was related to being male, having less experience, usually working non-day shifts, and perceiving staffing or resources as inadequate, nurse-physician relations as poor, and co-worker and supervisor support as low. Associations between abuse and staffing or resource inadequacy and poor working relations persisted when controlling for personal and job characteristics. INTERPRETATION: Modifiable factors are important to nurses' on-the-job safety.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score0.188

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 science0.0000.000
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 head0.024
GPT teacher head0.264
Teacher spread0.240 · 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

Citations123
Published2009
Admission routes2
Has abstractyes

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