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Record W3005880575 · doi:10.5430/jha.v9n1p26

A critical appraisal of what organisational approaches are pivotal to improve patient safety

2020· article· en· W3005880575 on OpenAlexvenueno aff
Jeong‐Ah Kim, Daniel Terry, Sunny Jang, Julia Gilbert, Mary Cruickshank

Bibliographic record

VenueJournal of Hospital Administration · 2020
Typearticle
Languageen
FieldHealth Professions
TopicQuality and Safety in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsSafety culturePatient safetyHealth careOrganizational cultureCritical appraisalWork (physics)NursingMedicineKnowledge managementBusinessPublic relationsComputer sciencePolitical scienceEngineeringManagementAlternative medicine

Abstract

fetched live from OpenAlex

Background: Patient safety remains a priority for healthcare organisations globally. There remains little consensus regarding the extent of this issue and the resultant impact on both individuals and communities. Aim: Our study aims to provide healthcare organisations and decision makers with increased information regarding predictive risk factors to enhance patient safety, and develop an organisational culture of safety. Methods: This paper reviews current literature regarding patient safety and presents predictive risk factors and recommendations for healthcare organisations globally to measure and monitor patient safety. Results: Three categories of organisational factors promoting safety culture were identified – Focusing on system/culture, management support and team work and event reporting. Conclusions: This review strove to identify and discuss the predictive risk factors for patient safety and support the importance of a positive organisational culture and strong leadership in monitoring and reducing patient care errors and improving patient care in healthcare setting.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.093
GPT teacher head0.413
Teacher spread0.320 · 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 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

Citations1
Published2020
Admission routes1
Has abstractyes

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