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Record W7062672681

Workplace violence and intentions to quit: results from a survey of London nurses

2008· other· en· W7062672681 on OpenAlexfundno aff

Bibliographic record

VenueGreenwich Academic Literature Archive (University of Greenwich) · 2008
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersMedical Research CouncilSouth African Medical Research CouncilSigma Theta Tau InternationalMcGill University
KeywordsHarassmentVerbal abuseWorkplace violenceAffect (linguistics)Logistic regressionOccupational safety and healthJob satisfactionDescriptive statisticsTest (biology)
DOInot available

Abstract

fetched live from OpenAlex

Study Objective: \nWork-place violence, harassment and abuse is an increasing feature of nurses’ experience of work in many countries. There is some evidence that the experience of workplace violence affects levels of job satisfaction (Hesketh et al 2003) and career decisions (e.g. Mayer et al 1999, Fernandes \net al 1999). This paper reports on verbal and physical abuse by patients, relatives and carers, as well as racial and sexual harassment in Acute Hospitals in London and investigates whether workplace violence affects nurses’ intentions to leave either their current job or the nursing \nprofession, controlling for a number of other factors that are known to affect career decisions, such as workload, pay and own health. \nMethod: \nA questionnaire designed by two of the authors (Reeves and West) to assess many different aspects of nurses work life was used in a postal survey of nurses grades A to I practising in twenty London acute trusts in 2002. A total of 6,160 clinical nurses were mailed the questionnaires and 2,880 returned completed questionnaires, resulting in an overall response rate of 47%, discounting undelivered questionnaires. Respondents worked in a wide variety of clinical settings but mainly in acute medical and surgical wards. In addition to descriptive statistics, results were analysed using logistic regression with robust standard errors: the appropriate test when the dependent variable is dichotomous and the individual respondents clustered within units (nurses working within hospitals are not statistically independent). \nResults: \nOur results show high levels of racial (%), sexual (%) and other, unspecified forms of harassment (%), as well as verbal and physical abuse (14% had been physically assaulted with 5% being assaulted more than once), over the previous 6 months. A very small number (1%) reported \nexperiencing all three forms of harassment; 12% two forms and 29% one form. Only 45% of this sample intended to stay in nursing for at least 3 years; 40% were undecided and 15% intended to leave. Logistic regression estimates showed that reported levels of abuse and harassment had a significant impact on respondents’ career intentions, even in models that controlled for known factors affecting career decisions. About 70% of our respondents reported that they had had too little training in dealing with aggressive behaviour—or none at all—but there was no statistical relationship between lack of training and reported assaults. \nConclusions: \nThe international shortage of health care workers is due at least in part to low retention rates. It is crucial to investigate nurses’ experiences of work to identify the factors that shape their career decisions. Workplace violence is increasingly acknowledged as an international, service-wide, health care problem. This paper adds to the literature that shows that workplace violence has an \nimpact on nurses’ career decisions. The implications for managers and policy makers are that strengthening systems of security and providing nurses with training in interpersonal relationships including dealing with aggressive patients could slow nurse turnover.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.038
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.012
GPT teacher head0.235
Teacher spread0.222 · 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 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

Citations0
Published2008
Admission routes1
Has abstractyes

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