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Record W3019675670 · doi:10.1017/cem.2020.380

Going to the COVID-19 Gemba: Using observation and high reliability strategies to achieve safety in a time of crisis

2020· article· en· W3019675670 on OpenAlexaff
Jennifer Thull‐Freedman, Shawn Mondoux, Antonia Stang, Lucas B. Chartier

Bibliographic record

VenueCanadian Journal of Emergency Medicine · 2020
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsUniversity of TorontoUniversity Health NetworkMcMaster UniversityAlberta Children's HospitalSt. Joseph’s Healthcare HamiltonUniversity of Calgary
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Reliability (semiconductor)2019-20 coronavirus outbreakContent (measure theory)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicineAction (physics)VirologyMathematicsOutbreak

Abstract

fetched live from OpenAlex

Implementation of high reliability principles in healthcare delivery is recognized as an effective strategy for reducing harm to patients and healthcare workers. ith the coronavirus disease 2019 (COVID-19) pandemic upon us, our emergency departments (EDs) are facing an unprecedented safety threat. How does a high reliability ED function during a pandemic, and what are the most important strategies for keeping ourselves and our patients safe?

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.272
GPT teacher head0.453
Teacher spread0.181 · 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

Citations22
Published2020
Admission routes1
Has abstractyes

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