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Record W1988149828 · doi:10.12927/hcq.2012.22765

Work Life and Patient Safety Culture in Canadian Healthcare: Connecting the Quality Dots Using National Accreditation Results

2012· article· en· W1988149828 on OpenAlexaffabout
Jonathan Mitchell

Bibliographic record

VenueHealthcare Quarterly · 2012
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Management
Canadian institutionsCARE Canada
Fundersnot available
KeywordsAccreditationOrganizational culturePatient safetyHealth careQuality managementBest practiceWork (physics)Public relationsNursingQuality (philosophy)Total quality managementBusinessProcess (computing)Process managementMedicineMedical educationPolitical scienceEngineeringMarketingComputer science

Abstract

fetched live from OpenAlex

Fostering quality work life is paramount to building a strong patient safety culture in healthcare organizations. Data from two patient safety culture and work-life questionnaires used for Accreditation Canada's national program were analyzed. Strong team leadership was reported in that units were doing a good job of identifying, assessing and managing risks to patients. Seventy-one percent of respondents gave their unit a positive overall grade on patient safety, and 79% of respondents felt that they could often do their best-quality work in their job. However, healthcare workers felt that they did not have enough time to do their jobs adequately and indicated that co-workers were cutting corners in patient care in order to save time. This article discusses engaging both senior leadership and the entire organization in the change process, ensuring supervisory support, and using performance measures to focus organizational efforts on key priorities all as improvement strategies relevant to these findings. These strategies can be used by organizations across sectors and jurisdictions and by healthcare leaders to positively affect work life and patient safety.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.159
GPT teacher head0.467
Teacher spread0.308 · 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

Citations11
Published2012
Admission routes2
Has abstractyes

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