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Record W2017957164 · doi:10.1108/14777270610647029

Developing large group working in clinical governance

2006· article· en· W2017957164 on OpenAlex
Debbie Wall, Kathy Dickinson, Jackie Kilbane, Dave Cummings

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.

Bibliographic record

VenueClinical Governance An International Journal · 2006
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Management
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsFacilitationContext (archaeology)OriginalityCorporate governanceService (business)Clinical governanceGroup (periodic table)Work (physics)Process managementHealth careOperations managementKnowledge managementPsychologyBusinessComputer scienceEngineeringPolitical scienceMarketingSocial psychology

Abstract

fetched live from OpenAlex

Purpose – To report on how service changes can be accelerated by working with large groups that represent all parts of a complete healthcare service or care pathway, during specific events, and using well‐defined facilitation techniques. Design/methodology/approach – Case examples are cited from the Clinical Governance Support Team's “protected time” programme and subsequent work, and specific quotes and examples from large group events are used to describe the potential impact of the approach. Findings – Established group facilitation techniques can be adapted for use in the context of a large group representative of a whole clinical system or pathway, to accelerate service improvement. Originality/value – The paper reports on the practical findings from Clinical Governance Support Team group facilitators working on large group events from a number of UK NHS Trusts.

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)0.012
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.299
GPT teacher head0.590
Teacher spread0.291 · 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