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Record W2344064505 · doi:10.1108/jhom-12-2014-0206

Using systems thinking to support clinical system transformation

2016· article· en· W2344064505 on OpenAlexafffund
Allan Best, Alex Berland, Carol P. Herbert, Jennifer Bitz, Marlies W van Dijk, Christina M. Krause, D. Douglas Cochrane, Kevin Noel, Julian Marsden, Shari McKeown, John S. Millar

Bibliographic record

VenueJournal of Health Organization and Management · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsBC Innovation CouncilCanadian Patient Safety InstituteNutrasource
FundersMichael Smith Health Research BC
KeywordsProcess managementContext (archaeology)Scope (computer science)Change management (ITSM)Knowledge managementHealth careSystems thinkingProcess (computing)Action researchScale (ratio)Computer scienceManagement sciencePsychologyOperations managementBusinessPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Purpose - The British Columbia Ministry of Health's Clinical Care Management initiative was used as a case study to better understand large-scale change (LSC) within BC's health system. Using a complex system framework, the purpose of this paper is to examine mechanisms that enable and constrain the implementation of clinical guidelines across various clinical settings. Design/methodology/approach - Researchers applied a general model of complex adaptive systems plus two specific conceptual frameworks (realist evaluation and system dynamics mapping) to define and study enablers and constraints. Focus group sessions and interviews with clinicians, executives, managers and board members were validated through an online survey. Findings - The functional themes for managing large-scale clinical change included: creating a context to prepare clinicians for health system transformation initiatives; promoting shared clinical leadership; strengthening knowledge management, strategic communications and opportunities for networking; and clearing pathways through the complexity of a multilevel, dynamic system. Research limitations/implications - The action research methodology was designed to guide continuing improvement of implementation. A sample of initiatives was selected; it was not intended to compare and contrast facilitators and barriers across all initiatives and regions. Similarly, evaluating the results or process of guideline implementation was outside the scope; the methods were designed to enable conversations at multiple levels - policy, management and practice - about how to improve implementation. The study is best seen as a case study of LSC, offering a possible model for replication by others and a tool to shape further dialogue. Practical implications - Recommended action-oriented strategies included engaging local champions; supporting local adaptation for implementation of clinical guidelines; strengthening local teams to guide implementation; reducing change fatigue; ensuring adequate resources; providing consistent communication especially for front-line care providers; and supporting local teams to demonstrate the clinical value of the guidelines to their colleagues. Originality/value - Bringing a complex systems perspective to clinical guideline implementation resulted in a clear understanding of the challenges involved in LSC.

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)0.024
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.003
Science and technology studies0.0060.023
Scholarly communication0.0150.010
Open science0.0030.010
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.581
GPT teacher head0.656
Teacher spread0.075 · 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 designTheoretical or conceptual
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

Citations41
Published2016
Admission routes2
Has abstractyes

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