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Record W2745681188 · doi:10.12927/hcq.2017.25233

New Tools for Measuring and Improving Patient Safety in Canadian Hospitals

2017· article· en· W2745681188 on OpenAlexaffabout
Jennifer D’Silva, Joseph Emmanuel Amuah, Vanessa Sovran, Anne MacLaurin, Jennifer Rodgers, Tracy Johnson, Kira Leeb, Sandi Kossey

Bibliographic record

VenueHealthcare Quarterly · 2017
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsCanadian Patient Safety InstituteCanadian Institute for Health Information
Fundersnot available
KeywordsPatient safetyBest practiceHealth administrationMedicineBusinessMedical emergencyNursingHealth carePublic healthPolitical science

Abstract

fetched live from OpenAlex

The Canadian Institute for Health Information (CIHI) and the Canadian Patient Safety Institute (CPSI) have collaborated on a new measure of patient safety, along with a resource of evidence-informed practices. This measure captures four broad categories of harm in acute care hospitals, consisting of 31 clinical groups selected by clinicians. Analysis showed that harm was experienced in 1 of 18 hospital stays in Canada in 2014ߝ2015 and that no single category accounted for the majority of harmful events. Although CIHI and CPSI continue to work with hospitals and experts to further refine the methodology, the measure and associated Improvement Resource are useful new tools for monitoring and identifying harm, and have the potential to improve 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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.858
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.076
GPT teacher head0.409
Teacher spread0.333 · 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 designOther design
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
Published2017
Admission routes2
Has abstractyes

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