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Record W1978707451 · doi:10.1186/s12913-014-0642-x

One size does not fit all: a qualitative content analysis of the importance of existing quality improvement capacity in the implementation of Releasing Time to Care: the Productive Ward™ in Saskatchewan, Canada

2014· article· en· W1978707451 on OpenAlexafffundabout
Jessica Hamilton, Tanya Verrall, Jill Maben, Peter Griffiths, Kyla Avis, G. Ross Baker, Gary Teare

Bibliographic record

VenueBMC Health Services Research · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of TorontoSaskatchewan Health Quality Council
FundersCanadian Institutes of Health ResearchSaskatchewan Health Research Foundation
KeywordsUnit (ring theory)Health administrationQuality managementNursing researchMedicineQuality (philosophy)NursingHealth informaticsQualitative researchChristian ministryFocus groupWork (physics)Organizational cultureContent analysisHealth careProcess managementPublic healthOperations managementPublic relationsBusinessPsychologyMarketingEngineeringSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Releasing Time to Care: The Productive Ward™ (RTC) is a method for conducting continuous quality improvement (QI). The Saskatchewan Ministry of Health mandated its implementation in Saskatchewan, Canada between 2008 and 2012. Subsequently, a research team was developed to evaluate its impact on the nursing unit environment. We sought to explore the influence of the unit's existing QI capacity on their ability to engage with RTC as a program for continuous QI. METHODS: We conducted interviews with staff from 8 nursing units and asked them to speak about their experience doing RTC. Using qualitative content analysis, and guided by the Organizing for Quality framework, we describe the existing QI capacity and impact of RTC on the unit environment. RESULTS: The results focus on 2 units chosen to highlight extreme variation in existing QI capacity. Unit B was characterized by a strong existing environment. RTC was implemented in an environment with a motivated manager and collaborative culture. Aided by the structural support provided by the organization, the QI capacity on this unit was strengthened through RTC. Staff recognized the potential of using the RTC processes to support QI work. Staff on unit E did not have the same experience with RTC. Like unit B, they had similar structural supports provided by their organization but they did not have the same existing cultural or political environment to facilitate the implementation of RTC. They did not have internal motivation and felt they were only doing RTC because they had to. Though they had some success with RTC activities, the staff did not have the same understanding of the methods that RTC could provide for continuous QI work. CONCLUSIONS: RTC has the potential to be a strong tool for engaging units to do QI. This occurs best when RTC is implemented in a supporting environment. One size does not fit all and administrative bodies must consider the unique context of each environment prior to implementing large-scale QI projects. Use of an established framework, like Organizing for Quality, could highlight the distinctive supports needed in particular care environments to increase the likelihood of successful engagement.

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)0.041
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0410.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.004
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.650
GPT teacher head0.658
Teacher spread0.008 · 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

Citations25
Published2014
Admission routes3
Has abstractyes

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