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Record W2144644708 · doi:10.5539/gjhs.v5n4p211

Health Care Violence and Abuse towards Nurses in Hospitals in North of Iran

2013· article· en· W2144644708 on OpenAlexvenueno aff
Mohammad Khademloo, Fatemeh Sheikh Moonesi, Hamed Gholizade

Bibliographic record

VenueGlobal Journal of Health Science · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsnot available
FundersMazandaran University of Medical Sciences
KeywordsVerbal abuseMedicineFamily medicinePhysical abuseHealth carePopulationCross-sectional studyPsychiatryDomestic violenceSuicide preventionPoison controlEnvironmental health

Abstract

fetched live from OpenAlex

AIM: In the current survey, we explored the prevalence of verbal and physical abuse against the nurses in different hospitals of north of Iran. METHODS: We performed a cross-sectional survey. Nurses were interviewed using a standardized questionnaire (Staff Observation Scale Revised (SOAS-R)). The sample covered 400 participants from 5 hospitals of Mazandaran University of medical sciences, Sari, Iran. RESULTS: The sampling size involved 271 participants (271 forms from 400 forms (sampling population) were filled completely) including 193 female (71.2 %) and 78 male (28.8 %) participants. 79 (29.1%) participants experienced physical abuse and 260 (95.9%) participants were abused verbally. It was noted that in 35 cases (44.3%) the patients were the source of physical abuse and in 44 cases (55.6%) the members of patients' family were the source. In 79 (30.3%) cases the patients were the source of verbal violence and in 139 (53.4%) cases the members of patients' family were the source and in 42 (16.1%) cases coworkers were the sources. CONCLUSION: Verbal abuse was a common type of violence in our study in north of Iran. There is a requirement to increase awareness about this significant problem among health care workers.

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)0.003
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 score0.144
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.015
GPT teacher head0.351
Teacher spread0.336 · 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

Citations40
Published2013
Admission routes1
Has abstractyes

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