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Role stress amongst nurses at the workplace: concept analysis

2011· article· en· W1649160783 on OpenAlexaff
Sanaz Riahi

Bibliographic record

VenueJournal of Nursing Management · 2011
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsOntario Shores Centre for Mental Health Sciences
FundersWorld Health Organization
KeywordsBurnoutWorkloadVulnerability (computing)NursingDistressPsychologyStress (linguistics)Nursing theoryNursing literatureFormal concept analysisNursing managementMedicineMEDLINEClinical psychologyAlternative medicineManagementComputer sciencePolitical science

Abstract

fetched live from OpenAlex

AIM: The present study explicates the concept of role stress amongst nurses through an analysis adopted from Walker and Avant; Strategies for Theory Construction in Nursing, 4th edn, Prentice Hall, New Jersey, NY. BACKGROUND: Role stress has become a significant problem amongst nurses and has created much distress leading to burnout among many in the nursing profession. It is significant to analyse the concept of role stress and its relative attributes and consequences, in order to recognize the necessary antecedents needed to create better conditions for nurses at the workplace. EVALUATION: A modified method developed by Walker and Avant was used for this concept analysis. KEY ISSUES: A model representing the concept of role stress was developed through careful consideration of the attributes, consequences, antecedents and empirical referents of role stress. CONCLUSION: The concept analysis of role stress among nurses at the workplace recognized the vulnerability of the nursing discipline towards burnout and distress in general. IMPLICATIONS FOR NURSING MANAGEMENT: It is critical to be aware of the current state of health care and note the increased workload created for nurses. Nurses are at a greater vulnerability for role stress, making it imperative for health care organizations to critically evaluate and establish preventative measures for the concept of role stress.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.487
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.305
Teacher spread0.281 · 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 designNot applicable
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

Citations60
Published2011
Admission routes1
Has abstractyes

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