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Record W2046325647 · doi:10.12927/hcq.2008.19643

An Evaluation of Patient Safety Leadership Walkarounds

2008· article· en· W2046325647 on OpenAlexaff
Rosanne Zimmerman, Ivan K. Ip, Charlotte Daniels, Teresa Smith, Jill Shaver

Bibliographic record

VenueHealthcare Quarterly · 2008
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsHamilton Health Sciences
Fundersnot available
KeywordsPatient safetySafety cultureMedicineNursingPatient satisfactionWork (physics)Medical educationOrganizational cultureHealth carePublic relationsManagementEngineeringPolitical science

Abstract

fetched live from OpenAlex

Patient safety leadership walkarounds (PSLWA) have been identified as an effective tool to improve patient safety culture. At Hamilton Health Sciences, after one year of monthly PSLWA in all clinical and service programs, 1,351 patient safety issues were identified, of which 64-80% have been resolved or have active improvement work in progress. Five hundred staff were invited to complete a process evaluation regarding the effectiveness of the current process of PSLWA. A total of 341 surveys were returned (68%). The overall evaluation demonstrated satisfaction with the process of PSLWA; 93% of those surveyed reported that they felt comfortable openly and honestly discussing patient safety issues and had an enhanced awareness of patient safety. Five areas of opportunity for process improvement were identified: scheduling, scripts, feedback, reporting and resolving issues deferred for an organization approach. PSLWA have offered an effective way to engage leadership and staff in open discussions about patient safety and collaborative approaches for solutions suggesting an enhanced patient 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.592

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 integrity0.0000.000
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 head0.262
GPT teacher head0.451
Teacher spread0.190 · 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

Citations26
Published2008
Admission routes1
Has abstractyes

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