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Record W3108807245 · doi:10.5267/j.msl.2020.10.014

Integration model of total quality management and six sigma in hospital quality management

2020· article· en· W3108807245 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueManagement Science Letters · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Optimization Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsStructural equation modelingConfirmatory factor analysisTotal quality managementLatent variableSix SigmaProcess managementQuality managementOperations managementQuality (philosophy)Computer scienceService (business)Knowledge managementBusinessManagement systemEngineeringMarketing

Abstract

fetched live from OpenAlex

Implementation of quality management is very important for hospitals to improve processes, solve problems, and reduce variations and errors in service, including through the implementation of popular Total Quality Management (TQM) and Six Sigma (SS) as new quality management strategies to increase profitability, effectiveness and efficiency of the organization's operations to meet customer’s needs. This study aims to develop an integrated hospital quality management model from the practice of TQM and SS to provide synergy in improving hospital performance. The study design was cross sectional through a survey using a questionnaire on 863 respondents, namely all employees ranging from doctors to administrative personnel at 8 hospitals. The TQM and SS practice integration model identified as “Quality Management Alliance Model (QMA)” consists of 6 variable constructs, namely: Management Practice (MP); TQM Infrastructure Practice (IPTQM); SS Infrastructure Practice (IPSS); Core Practice TQM (CPTQM); Core Practice SS (CPSS); and Hospital Performance (KRS) with 12 structural equations hypothesized. Data analysis are performed using Structural Equation Model through 2 tests, namely analysis of measurement models using confirmatory factor analysis (CFA) second order approach and structural model analysis. The results of the first order confirmatory factor analysis (CFA) analysis, after issuing invalid indicators (SLF≤0.5 and t≤1.96), obtained constructs of latent variables with models fit, valid, and reliable. Then in the second order CFA analysis on the overall model after being simplified through LVS (latent variable score) the study obtained construct model fit, valid and reliable. The results of the structural model analysis obtained a model fit with 11 structural equations that are positively and significantly related (t> 1.96). This study proves that the QMA model is feasible and can be applied to measure the implementation of hospital quality management. Hospital management is recommended to implement the QMA Model optimally to improve 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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.879
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0010.002
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 head0.026
GPT teacher head0.258
Teacher spread0.233 · 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