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Record W2032569042 · doi:10.12927/hcq..16625

Multi-Professional Mortality Review: Supporting a Culture of Teamwork in the Absence of Error Finding and Blame-Placing

2002· article· en· W2032569042 on OpenAlexaff
Kristine Jarvi, Roxana Sultan, Ainsley Lee, Frank Lussing, Rama Bhat

Bibliographic record

VenueHealthcare Quarterly · 2002
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsYork Central Hospital
Fundersnot available
KeywordsBlameTeamworkPatient safetyHealth careHuman errorSafety cultureHealth professionalsOrganizational cultureMedicineProcess (computing)WrongdoingMedical educationNursingPublic relationsPsychologyPolitical scienceManagementComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

Commitment to patient safety must be a priority of every healthcare institution. York Central Hospital has implemented a quality initiative to address multi-professional issues that result from a significant sentinel event where there is a notion of perceived wrongdoing due to an adverse and/or unexpected outcome--the Multi-Professional Mortality Review process. Unlike the traditional approach to professional review in healthcare, which results in a culture of error finding and blame-placing, this process acknowledges the fact that human errors can occur, reaffirms what is working well and ensures that steps are taken to mitigate the effects of the sentinel event under consideration. The review panel consists of healthcare professionals who have been involved in the case. The panel reviews the case and makes recommendations to senior clinical committees and hospital administration. The multi-professional review process has been met with a positive response at York Central Hospital and, to date, has served as a driving force behind the implementation of a number of systemic and professional changes.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.375

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 studies0.0000.000
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 head0.161
GPT teacher head0.480
Teacher spread0.318 · 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

Citations12
Published2002
Admission routes1
Has abstractyes

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