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Record W2946328941 · doi:10.1007/s40037-019-0511-8

Development and psychometric evaluation of an instrument to measure knowledge, skills, and attitudes towards quality improvement in health professions education: The Beliefs, Attitudes, Skills, and Confidence in Quality Improvement (BASiC-QI) Scale

2019· article· en· W2946328941 on OpenAlexaff
Allison Brown, Aditya Nidumolu, Meghan McConnell, Kent G. Hecker, Lawrence Grierson

Bibliographic record

VenuePerspectives on Medical Education · 2019
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsUniversity of CalgaryUniversity of OttawaMcMaster UniversityImpact
Fundersnot available
KeywordsRubricGeneralizability theoryExploratory factor analysisMedical educationContext (archaeology)Quality (philosophy)Scale (ratio)PsychologyHealth careMedicineConvergent validityConstruct validityCurriculumPsychometricsApplied psychologyClinical psychologyPedagogy

Abstract

fetched live from OpenAlex

INTRODUCTION: Health professionals are increasingly expected to foster and lead initiatives to improve the quality and safety of healthcare. Consequently, health professions education has begun to integrate formal quality improvement (QI) training into their curricula. Few instruments exist in the literature that adequately and reliably assess QI-related competencies in learners without the use of multiple, trained raters in the context of healthcare. This paper describes the development and psychometric evaluation of the Beliefs, Attitudes, Skills, and Confidence in Quality Improvement (BASiC-QI) instrument, a 30-item self-assessment tool designed to assess knowledge, skills, and attitudes towards QI. METHODS: Sixty first-year medical student participants completed the BASiC-QI and the Quality Improvement Knowledge Application Tool (QIKAT-R) prior to and immediately following a QI program that challenged learners to engage QI concepts in the context of their own medical education. Measurement properties of the BASiC-QI tool were explored through an exploratory factor analysis and generalizability study. Convergent validity was examined through correlations between BASiC-QI and QIKAT-R scores. RESULTS: Psychometric evaluation of BASiC-QI indicated reliability and validity evidence based on internal structure. Analyses also revealed that BASiC-QI scores were positively correlated with the scores from the QIKAT-R, which stands an indicator of convergent validity. CONCLUSION: BASiC-QI is a multidimensional self-assessment tool that may be used to assess beliefs, attitudes, skills, and confidence towards QI. In comparison with existing instruments, BASiC-QI does not require multiple raters or scoring rubrics, serving as an efficient, reliable assessment instrument for educators to examine the impact of QI curricula on learners.

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)0.024
metaresearch head score (Gemma)0.030
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.083
GPT teacher head0.498
Teacher spread0.415 · 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

Citations26
Published2019
Admission routes1
Has abstractyes

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