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Record W2105783797 · doi:10.12927/hcq.2008.19654

Communities of Practice: Creating Opportunities to Enhance Quality of Care and Safe Practices

2008· article· en· W2105783797 on OpenAlexafffundabout
Debbie White, Esther Suter, I J Parboosingh, Elizabeth Taylor

Bibliographic record

VenueHealthcare Quarterly · 2008
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of Calgary
FundersHealth CanadaUniversity of Alberta
KeywordsPatient safetyBest practiceSustainabilityBusinessQuality (philosophy)Clinical PracticeGood practiceQuality managementPatient careHealth careProcess managementNursingPublic relationsKnowledge managementMedical educationMedicineComputer scienceMarketingPolitical scienceEngineeringEngineering ethics

Abstract

fetched live from OpenAlex

A Communities of Practice (CoPs) approach was used to enhance interprofessional practice in seven clinical sites across Alberta. Participating staff were free to decide the area of practice to focus on and the actions to be implemented. All practice changes implemented by the CoPs related to either improving communications (e.g., introduction of joint care meetings) or information transfer (e.g., streamlining of admission and discharge processes). The practice changes contributed to more effective communication of information and more effective transitions of patients between providers, hence potentially reducing errors. The present study demonstrates that CoPs can enhance interprofessional communication and patient safety in traditional care delivery units. In contrast to more structured safety initiatives, sites were able to choose their area of focus. This ensures buy-in and enhances sustainability, making CoPs an interesting option for patient safety initiatives.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0080.005
Scholarly communication0.0030.002
Open science0.0020.011
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.164
GPT teacher head0.547
Teacher spread0.383 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations25
Published2008
Admission routes3
Has abstractyes

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