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Record W4250221891 · doi:10.1504/ijpqm.2020.110939

Interactions dynamics between the performance management system and the quality management system

2020· article· en· W4250221891 on OpenAlexaff
Mira Thoumy, Marie Hélène Jobin

Bibliographic record

VenueInternational Journal of Productivity and Quality Management · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Quality and Management
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsQuality management systemQuality managementQuality (philosophy)Dynamics (music)Independence (probability theory)Management systemSystem dynamicsTotal quality managementProcess managementKnowledge managementOperations managementComputer scienceEngineering managementEngineeringPsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

The purpose of this study is to characterise the dynamics between the performance management system (PMS) and the quality management system (QMS). We conducted a comparative case study between two university medical centres. We interviewed 41 participants from eight sub-units of analysis. We also conducted seven hours of meeting observations, 16 hours of management observations as well as a review of 46 documents that were submitted to us by the workers. Our results show that there are four types of possible interactions between the PMS and the QMS which are independence, antagonism, communication and collaboration. Moreover, there are three realms of interactions being the realms of structure, identities and practices. We found that the PMS and the QMS interact more in clinical units. The support services do not have well-developed performance and quality management. Also, the workers involved in operations are disconnected from the concept of 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 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)0.019
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.036
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0080.010
Scholarly communication0.0140.008
Open science0.0010.009
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 head0.133
GPT teacher head0.450
Teacher spread0.317 · 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 designObservational
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

Citations3
Published2020
Admission routes1
Has abstractyes

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