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Development of Communities of Practice to Facilitate Quality Improvement Initiatives in Surgical Oncology

2008· article· en· W2325844763 on OpenAlex
Michael Fung‐Kee‐Fung, Elena Goubanova, Karen Sequeira, Rose Cook, Claire Crossley, Bernard Langer, Andrew J. Smith, Hartley Stern

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueQuality Management in Health Care · 2008
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of OttawaMinistry of Health and Long Term Care
Fundersnot available
KeywordsAccreditationProcess managementProcess (computing)Quality (philosophy)Quality managementHealth careKnowledge managementEngineering managementBusinessMedicineComputer scienceMedical educationEngineeringManagement systemOperations managementPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: The process of developing clinical guidelines and standards for cancer treatment and screening is well established in the Ontario health care system; however, the dissemination and implementation of such guidelines and standards are more recent undertakings. Traditional implementation strategies to improve surgical practice and the delivery of cancer care have not been consistently effective. There is a recognized need to develop integrated models that offer direct support for implementation strategies. Such a model should be feasible, adaptable, and open to evaluation across diverse surgical settings. DISCUSSION: Research suggests that successful implementation should consider tools and expertise from other disciplines. This article considers a community of practice (COP) model to provide a supportive infrastructure for quality improvements in cancer surgery. The COP model was adapted for cancer surgeons. It is supported by 5 enablers referred to as tools: communication system, project development support, access to data, access to evidence review, and accreditation with continued medical education and continued professional development. These tools need to be part of an infrastructure that is both provided and supported by a team of administrators and health care professionals, who have active roles and responsibilities. Therefore, the primary objective of this article is to describe our COP model in cancer surgery including the key success factors necessary for providing the infrastructure and tools. The secondary objective is to offer the integrated COP model as a basis for future research and the evaluation of various collaborative improvement projects. SUMMARY: Building on knowledge management concepts, we identified the 4 essential processes that should be targeted by implementation strategies. A common COP evaluation framework uses the outcomes of 4 knowledge conversion modes-organizational memory, social capital, innovation, and knowledge transfer-as proxies for actual provider and organizational behavior. Insights from different collaborative improvement projects described in a consistent way could inform future research and assist in the collation of systematic reviews on this topic.

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.

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)0.018
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.774
GPT teacher head0.702
Teacher spread0.072 · 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