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Record W1980093279 · doi:10.1002/job.154

The effects of quality improvement practices on team effectiveness: a mediational model

2002· article· en· W1980093279 on OpenAlexaff
Louise Lemieux‐Charles, Michael Murray, G. Ross Baker, Jan Barnsley, Kevin Tasa, Salahadin A. Ibrahim

Bibliographic record

VenueJournal of Organizational Behavior · 2002
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsInstitute for Work & HealthMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsAffect (linguistics)Structural equation modelingTeam effectivenessPsychologyQuality (philosophy)Quality managementTeam compositionOrganizational performanceHealth carePerceptionApplied psychologyKnowledge managementSocial psychologyBusinessMarketingComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Abstract Researchers have been challenged to specify the processes that quality improvement (QI) practices could be expected to generate and to explain how they might contribute to organizational effectiveness. This research article meets that challenge through a study of 97 teams in the health care field. The authors developed a ‘Quality Improvement Practices Index’ and showed that QI practices could be differentiated from traditional team‐level variables, and that such practices affect both directly and indirectly (through team‐level variables) team effectiveness. Two models were tested using structural equation modelling. It was found that the perceptions of the impact of QI practices on team effectiveness varied depending on who was assessing the team's performance—members of the team or managers who were external to the team but responsible for the team's performance. The authors discuss the implications of these results both for researchers and practitioners. Copyright © 2002 John Wiley & Sons, Ltd.

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)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.651
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.027
GPT teacher head0.287
Teacher spread0.260 · 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 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

Citations74
Published2002
Admission routes1
Has abstractyes

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