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Record W2032129332 · doi:10.12927/hcq.2014.23879

Using the Accreditation Journey to Achieve Global Impact: UHN’s Experience at the Kuwait Cancer Control Centre

2014· article· en· W2032129332 on OpenAlexaboutno aff
Nafeesa Ladha-Waljee, Stephen Mcateer, Veronica Nickerson, Adil Khalfan

Bibliographic record

VenueHealthcare Quarterly · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Management
Canadian institutionsnot available
Fundersnot available
KeywordsAccreditationMandateGeneral partnershipMedicineCancerQuality managementBest practiceNursingControl (management)Family medicineLibrary scienceMedical educationManagementPolitical scienceOperations managementEngineeringInternal medicineComputer science

Abstract

fetched live from OpenAlex

On January 1, 2011, Princess Margaret Cancer Centre (PM) - University Health Network (UHN) began a five-year partnership agreement with the Kuwait Ministry of Health's Kuwait Cancer Control Center (KCCC) to enhance cancer care services. Over the course of the partnership, opportunities for improvement were identified by UHN experts in order to accelerate KCCC's development toward subspecialty cancer care. Many of these opportunities involved building a robust infrastructure to support foundational hospital operation processes and procedures. Harnessing UHN's own successes in accreditation, the partnership took advantage of the national accreditation mandate in Kuwait to initiate a quality program and drive clinical improvement at KCCC. This resulted in improved staff engagement, better awareness and alignment of administration with clinical management and a stronger patient safety culture. This article discusses the successes and lessons learned at KCCC that may provide insight to healthcare providers implementing Accreditation Canada International's accreditation framework in other countries and cultures.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.147
GPT teacher head0.514
Teacher spread0.368 · 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 designQualitative
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

Citations6
Published2014
Admission routes1
Has abstractyes

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