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Record W2589361024 · doi:10.12927/cjnl.2016.24985

Implementing a Just Culture: Perceptions of Nurse Managers of Required Knowledge, Skills and Attitudes

2017· article· en· W2589361024 on OpenAlexaffvenue
Michelle Freeman, Linda Morrow, Margo Cameron, Karen Zink McCullough

Bibliographic record

VenueNursing leadership · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsWindsor Regional HospitalUniversity of Windsor
Fundersnot available
KeywordsOrganizational culturePerceptionNursingPsychologyHealth careNurse AdministratorOrder (exchange)Public relationsMEDLINEMedicineBusinessPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Healthcare organizations have been challenged to create a just culture as part of their culture of safety. PURPOSE: To explore perceptions of nurse managers in developing personal competencies in order to enable them to effectively implement a just culture in their units. METHOD: Qualitative content analysis of semi-structured interviews with nine nurse managers identified themes. Data were independently analyzed by three members of the research team. FINDINGS: Analysis of interview transcripts identified the following four themes: need for education of managers and employees, need for a variety of new skills for nurse managers, need to change attitudes from the long-standing punitive culture and fault of individual and challenges in implementation because of time constraints. CONCLUSION: Implementing a just culture is complex. Education of nurse managers is crucial. A series of educational strategies is recommended. Findings support the need for new competencies to enable nurse managers to effectively implement a just culture in their units.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.150
GPT teacher head0.408
Teacher spread0.259 · 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.

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

Citations10
Published2017
Admission routes2
Has abstractyes

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