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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".