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Record W1995860095 · doi:10.12927/hcq.2008.19657

Canadian Patient Safety Champions: Collaborating on Improving Patient Safety

2008· article· en· W1995860095 on OpenAlexaffabout
Katharina Kovacs Burns

Bibliographic record

VenueHealthcare Quarterly · 2008
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPatient safetyBest practiceMedicineNursingMedical emergencyBusinessHealth carePolitical science

Abstract

fetched live from OpenAlex

Patients for Patient Safety Canada champions have grown in numbers and purpose since their initiation into the World Health and Pan-American Health Organizations' Patients for Patient Safety initiative in May 2006. The 25 Canadian patients and family members not only share their adverse event experiences but are actively engaged in collaboration with health professionals, administrators and decision-makers to initiate proactive patient safety strategies. Their intention is to have their stories heard as tools for learning. They also wish to raise local, regional and national awareness of patient safety problems. The different patient and family stories and experiences share some common issues and suggested solutions that might make a difference in patient safety. One key solution is involving patients and families not only in discussions of treatment and follow-up when adverse events occur but also proactively on patient safety advisory committees. These actions would acknowledge a common interest in seeing that the right things are done. Patients and families share the common interest of all those advocating for patient safety, namely, First do no harm (attributed to Hippocrates, circa 470-360 B.C.). The patients and families of Patients for Patient Safety Canada are a group of committed, dedicated individuals who should be acknowledged for sharing their experiences and trying to make a difference in 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.789
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.040
GPT teacher head0.343
Teacher spread0.304 · 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 designNot applicable
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

Citations20
Published2008
Admission routes2
Has abstractyes

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