Reliability and construct validation of the Blended Learning Usability Evaluation–Questionnaire with interprofessional clinicians in Canada: a methodological study
Bibliographic record
Abstract
PURPOSE: To generate Cronbach's alpha and further mixed methods construct validity evidence for the Blended Learning Usability Evaluation-Questionnaire (BLUE-Q). METHODS: Forty interprofessional clinicians completed the BLUE-Q after finishing a 3-month long blended learning professional development program in Ontario, Canada. Reliability was assessed with Cronbach's α for each of the 3 sections of the BLUE-Q and for all quantitative items together. Construct validity was evaluated through the Grand-Guillaume-Perrenoud et al. framework, which consists of 3 elements: congruence, convergence, and credibility. To compare quantitative and qualitative results, descriptive statistics, including means and standard deviations for each Likert scale item of the BLUE-Q were calculated. RESULTS: Cronbach's α was 0.95 for the pedagogical usability section, 0.85 for the synchronous modality section, 0.93 for the asynchronous modality section, and 0.96 for all quantitative items together. Mean ratings (with standard deviations) were 4.77 (0.506) for pedagogy, 4.64 (0.654) for synchronous learning, and 4.75 (0.536) for asynchronous learning. Of the 239 qualitative comments received, 178 were identified as substantive, of which 88% were considered congruent and 79% were considered convergent with the high means. Among all congruent responses, 69% were considered confirming statements and 31% were considered clarifying statements, suggesting appropriate credibility. Analysis of the clarifying statements assisted in identifying 5 categories of suggestions for program improvement. CONCLUSION: The BLUE-Q demonstrates high reliability and appropriate construct validity in the context of a blended learning program with interprofessional clinicians, making it a valuable tool for comprehensive program evaluation, quality improvement, and evaluative research in health professions education.
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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 | 0.067 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".