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Record W2024204074 · doi:10.1177/0733464813492582

Explaining the Success or Failure of Quality Improvement Initiatives in Long-Term Care Organizations From a Dynamic Perspective

2013· article· en· W2024204074 on OpenAlexaffabout
Francis Etheridge, Yves Couturier, Jean‐Louis Denis, Lucie Tremblay, Cara Tannenbaum

Bibliographic record

VenueJournal of Applied Gerontology · 2013
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsÉcole Nationale d'Administration PubliqueUniversité de MontréalUniversité de Sherbrooke
Fundersnot available
KeywordsPsychological interventionPerspective (graphical)Term (time)Long-term carePublic relationsQuality (philosophy)Quality managementNursingBusinessChange management (ITSM)Organizational changeSolidarityPsychologyMedicineMarketingPolitical scienceComputer science

Abstract

fetched live from OpenAlex

The purpose of this study was to better understand why change initiatives succeed or fail in long-term care organizations. Four case studies from Québec, Canada were contrasted retrospectively. A constipation and restraints program succeeded, while an incontinence and falls program failed. Successful programs were distinguished by the use of a change strategy that combined "let-it happen," "help-it happen," and "make-it happen" interventions to create senses of urgency, solidarity, intensity, and accumulation. These four active ingredients of the successful change strategies propelled their respective change processes forward to completion. This paper provides concrete examples of successful and unsuccessful combinations of "let-it happen," "help-it happen," and "make-it happen" change management interventions. Change managers (CM) can draw upon these examples to best tailor and energize change management strategies in their own organizations.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.183
GPT teacher head0.574
Teacher spread0.391 · 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 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

Citations18
Published2013
Admission routes2
Has abstractyes

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