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Record W2896037289 · doi:10.12927/cjnl.2018.25606

Attributes and Actions Required to Advance Quality and Safety in Hospitals: Insights from Nurse Executives

2018· article· en· W2896037289 on OpenAlexaffvenueabout
Lianne Jeffs, G. Ross Baker, Ru Taggar, Pam Hubley, Joy Richards, Jane Merkley, Judy Shearer, H. L. Webster, Melissa Dizon, Jessie Ho Fong

Bibliographic record

VenueNursing leadership · 2018
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsSinai Health SystemHospital for Sick ChildrenSouth Bruce Grey Health CentreInstitute for Work & HealthInstitute of Health Services and Policy ResearchUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsNursingQuality (philosophy)PsychologyPatient safetyBusinessMedicineHealth carePolitical science

Abstract

fetched live from OpenAlex

To lead effectively within their organizations, nurse executives must possess quality and safety literacy and be able to engage and motivate clinicians to participate in safety and quality initiatives. Given the paucity of research in Canada, a study was undertaken to explore nurse executives' understanding of the key concepts and strategies associated with patient safety and quality improvement, and their engagement with patient safety and quality improvement in their hospitals and healthcare systems. This study used an exploratory qualitative design with a content analysis approach on 20 nurse executives working in hospitals in Ontario. Three key themes emerged from the narrative data set including: (1) being a strategic and system thinker while possessing the emotional intelligence to influence staff; (2) building credibility and relationships with point-of-care staff, board of directors, and leadership team and (3) creating a culture of safety and high reliability. Study findings can be useful in informing future learning opportunities for nurse executives and nurses leaders at all levels to enhance their quality and safety literacy.

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)0.001
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 score0.253
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.372
GPT teacher head0.516
Teacher spread0.144 · 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

Citations10
Published2018
Admission routes3
Has abstractyes

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