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Record W2512903903 · doi:10.5430/jha.v5n6p1

Is patient safety sufficient in Japan? Differences in patient safety between Japan and the United States – Learning from the United States

2016· article· en· W2512903903 on OpenAlexvenueno aff
Masahiro Hirose

Bibliographic record

VenueJournal of Hospital Administration · 2016
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsnot available
FundersU.S. Department of Veterans Affairs
KeywordsMindsetPatient safetySAFERHealth careLicenseMedicineNursingFamily medicinePolitical science

Abstract

fetched live from OpenAlex

In Japan, patient safety has been promoted at all levels since the 1999 landmark adverse medical event at Yokohama City University Hospital (YCUH). However, patients do not believe that health care is becoming safer. Furthermore, two university hospitals (UHs) that were designated as “advanced treatment hospitals” had their status revoked by the Health Ministry as of June 1, 2015 due to patient safety problems. The history of patient safety in Japan can be roughly divided into two terms: 1999-2009 and since 2010. In the first term, a basic patient safety system was established that included the creation of a patient safety division and an incident-reporting system from the perspective of systems error rather than individual responsibility. Additionally, many companies have promoted the improvement and development of drugs and medical devices in collaboration with health care providers. The two recent serious medical errors at UHs seemed to occur partially due to a lack of medical ethics. Unlike in the United States (US), in Japan, there is no medical license renewal system, the organizations that govern physicians are weak, and the framework of lifelong education is inadequate. Therefore, the second term involves a mindset of quality-driven patient safety. It requires health care providers and policy makers to change their mindset toward medical ethics and patient safety by learning from the US and demands a strong organization and framework to govern physicians in Japan.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.037
GPT teacher head0.324
Teacher spread0.287 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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