Factor Structure Analysis of Pharmacy Students’ Performance on the Health Education Learning Environment Survey
Bibliographic record
Abstract
Objective. While high-quality learning environments are increasingly recognized as vital for health professions education programs and student success, there are no tools that have been validated for use within the pharmacy education context. This study seeks to assess whether the six-factor structure of the Health Education Learning Environment Survey (HELES) will replicate in a sample of pharmacy students. Methods. The study was conducted in a Doctor of Pharmacy program offered at a Western Canadian university. A sample of 288 pharmacy students, across two years of data collection, completed the 35-item HELES as an anonymous and online survey. Results. While the six-factor model of the HELES, as a whole, did not replicate through confirmatory factor analysis, a follow-up examination of unidimensionality for the individual subscales of the HELES showed that five of the six subscales met this requirement. One subscale, the work-life balance subscale, was better represented by the dimensions of time management and emotional well-being. Conclusion. These results provide preliminary support for the use of the HELES among pharmacy students, with additional research being needed to explore the work-life balance subscale and its appropriateness for this student group. In Canada, the HELES has the potential to fill an existing local and national gap in available program evaluation tools needed for gathering evidence on pharmacy program quality, strengths, and weaknesses, and to inform continuous quality improvement efforts and accreditation standards.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".