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Facilitating Change in Health Organizations

2013· article· en· W1666791234 on OpenAlexaffvenue
Chris Cavacuiti, Jennifer A. Locke

Bibliographic record

VenueHigher education of social science · 2013
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsTranstheoretical modelDilemmaValue (mathematics)Context (archaeology)Knowledge managementOrganizational changeConceptual modelPromotion (chess)Change management (ITSM)Computer scienceProcess (computing)Behavior changeProcess managementManagement sciencePsychologyPublic relationsBusinessMarketingPolitical scienceSocial psychologyEngineeringEpistemology

Abstract

fetched live from OpenAlex

Purpose: Significant time and effort are needed to facilitate organizational change; thus a well-constructed conceptual model may help health professionals identify and overcome the barriers impeding this process.Design / methodology / approach:Currently, there is no single framework for organizational change that has gained widespread acceptance. However, two well-validated organizational models are the Prochaska and DiClemente transtheoretical model and Green et al. al.’s health promotion model. In this paper we synthesize these models in the context of organizational change for a physician audience. Findings: We created a new model of organizational change that keeps the best elements of both the Prochaska and DiClemente transtheoretical model and Green et al. al.’s health promotion model. Furthermore, an example is illustrated using this approach.Originality / value: Most health organizations lack a consistent approach to managing change. As a result they have not been as effective in this area as they could be. Most previous organization change theorists have attempted to solve this dilemma by constructing new models of organizational change, which they hope will eventually become the dominant model. Our approach is original in that we have incorporated the best features of two pre-existing models. The value of this approach is that improving these existing models has a much greater potential for widespread acceptance than developing yet another new model.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.756
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.522
GPT teacher head0.680
Teacher spread0.158 · 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.

Study designObservational
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

Citations2
Published2013
Admission routes2
Has abstractyes

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