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Record W2983964783 · doi:10.1111/jnu.12527

Understanding Determinants of Sustainability Through a Realist Investigation of a Large‐Scale Quality Improvement Initiative (Lean): A Refined Program Theory

2019· article· en· W2983964783 on OpenAlexafffund
Rachel Flynn, Shannon D. Scott

Bibliographic record

VenueJournal of Nursing Scholarship · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of Alberta
FundersAlberta InnovatesHealth Research
KeywordsSustainabilityHealth careTheory of changeQuality managementQuality (philosophy)Scale (ratio)Management scienceProcess managementKnowledge managementPublic relationsPsychologyBusinessNursingSociologyPolitical scienceMedicineComputer scienceEngineeringMarketing

Abstract

fetched live from OpenAlex

BACKGROUND: Implementation science research seeks to understand ways to best ensure uptake of research-based initiatives to health care; however, there is little research done on how to sustain such efforts. Sustainability is the degree to which an initiative continues to be used in practice after efforts of implementation have ended. Sustainability research is a growing field of implementation science that needs further research to understand how to predict and measure the long-term use of effective initiatives to improve health care. The question of what influences the sustainability of research-based initiatives to improve health care remains unknown. PURPOSE: The purpose of this article was to present a refined program theory on the contextual factors and mechanisms that influence the sustainability of one large-scale quality management initiative (Lean) in pediatric health care. DESIGN: We conducted a multiphase realist investigation to explain under what contexts, for whom, how, and why Lean efforts are sustained or not sustained in pediatric health care through the generation of an explanatory program theory. METHODS: This article presents the theoretical triangulation of our multiphase realist investigation, resulting in a refined program theory. We integrated the initial program theories (IPTs) from each research phase to form a refined program theory. It involved going back and forth from the initial IPT to the findings from each phase and our middle-range theories and examining the most substantiated IPTs on the contextual factors and mechanisms that influenced the sustainability of Lean efforts. FINDINGS: The refined program theory depicts the complex nature to sustaining Lean efforts and that sustainability as a small, often unrepresentative portion of something much larger or more complex that cannot yet be seen or understood. The approach and nature of implementation is critical to shaping contexts for sustainability. Outcomes from implementation become facilitating or hindering contexts for sustainability. Customization to context is an important contextual factor for sustainability. Sense making, value congruency, and staff engagement are critical aspects from early implementation that enable or hinder processes of sustainment. Such mechanisms can trigger staff empowerment that can lead to a greater likelihood of sustainability. CONCLUSIONS: These findings have important implications for sustainability research, in understanding the determinants of sustainability of research-based initiatives in health care. CLINICAL RELEVANCE: It is important to understand and explain determinants of sustainability through theory-driven evaluative research in order to assist key stakeholders in sustaining the effective research-based initiatives made to improve healthcare services, patient care, and outcomes.

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)0.027
metaresearch head score (Gemma)0.044
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.044
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0030.015
Scholarly communication0.0040.010
Open science0.0030.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.701
GPT teacher head0.663
Teacher spread0.038 · 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 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".

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Citations20
Published2019
Admission routes2
Has abstractyes

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