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Record W4383876902 · doi:10.24928/2023/0163

Lean Health Check Framework

2023· article· en· W4383876902 on OpenAlexaff
Parastoo Eivazi Ziaei, Diana Salhab, Ritu Ahuja, Farook Hamzeh

Bibliographic record

VenueAnnual Conference of the International Group for Lean Construction · 2023
Typearticle
Languageen
FieldHealth Professions
TopicQuality and Safety in Healthcare
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Lean construction is an approach that prioritizes enhancing quality and value while minimizing waste.Lean management comprises of principles and concepts that must be observed to effectively implement Lean management and leverage its benefits.In line with Lean concepts and principles, appropriate behavior and culture must be properly implemented to achieve successful Lean management, hence necessitating the use of an established health check assessment to evaluate the level of Lean maturity.This study aims to introduce a health check assessment to ascertain the level of maturity of Lean behavior and culture in the construction industry.The health check assessment was formulated by identifying Lean success factors, which were further validated by Lean experts.The methodology employed to achieve the study objectives follows a Design Science Research (DSR) approach, which involves creating a health check framework and evaluating it through an expert panel interview among project parties in a real case study project.In addition to the Lean expert panel interview, other performance metrics, such as percent planned complete (PPC) and constraint information, were collected.The proposed framework was validated, and the results indicate that there may be a correlation between effective team communication and project performance.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score0.550

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 studies0.0010.000
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 head0.150
GPT teacher head0.450
Teacher spread0.299 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2023
Admission routes1
Has abstractyes

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