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Record W2150506071 · doi:10.12927/hcq.2010.21975

Assessment of Safety Culture Maturity in a Hospital Setting

2010· article· en· W2150506071 on OpenAlexaff
Madelyn Law, Rosanne Zimmerman, G. Baker, Teresa Smith

Bibliographic record

VenueHealthcare Quarterly · 2010
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSafety culturePatient safetyTeamworkMaturity (psychological)Organizational cultureBureaucracyBest practiceHealth careOccupational safety and healthProcess managementOperations managementNursingMedicineBusinessPublic relationsEngineeringPsychologyManagementPolitical science

Abstract

fetched live from OpenAlex

The Manchester Patient Safety Culture Assessment Tool (MaPSCAT) was used to examine the levels of safety culture maturity in four programs across one large healthcare organization. The MaPSCAT is based on a theoretical framework that was developed in the United Kingdom through extensive literature reviews and expert input. It provides a view of safety culture on 10 dimensions (continuous improvement, priority given to safety, system errors and individual responsibility, recording incidents, evaluating incidents, learning and effecting change, communication, personnel management, staff education and teamwork) at five progressive levels of safety maturity. These levels are pathological ("Why waste our time on safety?"), reactive ("We do something when we have an incident"), bureaucratic ("We have systems in place to manage safety"), proactive ("We are always on alert for risks") and generative ("Risk management is an integral part of everything we do"). This article highlights the use of a new tool, the results of a study completed with this tool and how the results can be used to advance safety culture.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.003
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 head0.026
GPT teacher head0.462
Teacher spread0.436 · 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 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

Citations27
Published2010
Admission routes1
Has abstractyes

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