MétaCan
Menu
Back to cohort
Record W3157193341 · doi:10.1108/ijpl-08-2020-0080

Creating a sustainable model for stroke system change

2021· article· en· W3157193341 on OpenAlexaffabout
Elizabeth Linkewich, Shelley Sharp, Denyse Richardson, Jocelyne McKellar

Bibliographic record

VenueInternational Journal of Public Leadership · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsToronto Rehabilitation InstituteUniversity Health NetworkOntario Stroke NetworkHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsTransformational leadershipChange management (ITSM)Process managementKnowledge managementProcess (computing)Theory of changeWork (physics)BusinessOriginalityOrganizational changeComputer sciencePublic relationsPolitical scienceEngineeringManagementSociologyMarketingQualitative research

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to develop an infrastructure and leadership capacity for a sustainable approach to collaborative change in a complex health-care system. Design/methodology/approach An infrastructure for system change and a mechanism to build capacity for change leadership was developed. This involved (1) using a community of a practice model to create a change community, (2) developing an iterative engagement and change process and (3) integrating collaborative change leadership skills and knowledge development within the process. Change leadership was evaluated using Wenger's phases of value creation. Findings A change community of 62 members across 19 organizations codeveloped a change process that aligns with Cooperrider's 4D Cycle. The change community demonstrated application of change leadership learnings throughout the change process. Originality/value A tailored approach was required to support sustainable transformational change in the Toronto stroke system. This novel methodology provides a framework for broader application to systems change in other complex systems that support both local and system-wide ownership of the work.

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)0.005
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.899
Threshold uncertainty score0.835

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.878
GPT teacher head0.647
Teacher spread0.230 · 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.

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

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Public LeadershipSame topicHealth Policy Implementation ScienceFrench-language works237,207