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Record W4401587201 · doi:10.1080/09537287.2024.2386432

Leaning on leadership? Understanding how a lean implementation impacts hospital workers’ performance

2024· article· en· W4401587201 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProduction Planning & Control · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsLean manufacturingBusinessOperations managementKnowledge managementProcess managementComputer scienceEngineering

Abstract

fetched live from OpenAlex

This study examines the extent to which a Lean implementation impacts hospital work unit performance. It also explores the extent to which willingness to change and role clarity explain these relationships. The longitudinal data for this study were collected from a Lean implementation study conducted in a Dutch hospital and then analyzed using structural equation modelling. The results reveal that the practice of Lean Leadership behaviour positively affects performance, both directly and indirectly through role clarity. However, other Lean practices, involvement in continuous improvement and Lean techniques, did not enhance hospital performance. The inconsistent findings concerning Lean’s soft practices suggest that major events (the COVID-19 pandemic in this case) can influence the implementation of Lean and the subsequent outcomes, potentially obstructing sustainable results. The novelty of this study is in its multi-wave design to evaluate the long-term effects of Lean and line managers’ leadership behaviour in a hospital context. Furthermore, it enhances our understanding of the mechanisms explaining the relationship between Lean and its outcomes in healthcare.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
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 head0.090
GPT teacher head0.284
Teacher spread0.195 · 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