Evaluating the Usability and Usefulness of an E-Learning Module for a Patient Clinical Information System at a Large Canadian Healthcare Organization
Bibliographic record
Abstract
Alberta Health Services (AHS) has introduced e-learning for health professionals to expand their existing training, offer flexible web-based learning opportunities, and reduce training time and cost. This study is designed to evaluate the usability and usefulness of an e-learning module for a patient clinical information system scheduling application. A cost-effective framework for usability evaluation has been developed and conceptualized as part of this research. Low-Cost Rapid Usability Engineering (LCRUE), Cognitive Task Analysis (CTA), and Heuristic Evaluation (HE) criteria for web-based learning were adapted and combined with the Software Usability Measurement Inventory (SUMI) questionnaire. To evaluate the introduction of the e-learning application, usability was assessed in two groups of users: frontline users and informatics consultant users. The effectiveness of the LCRUE, CTA, and HE when combined with the SUMI was also investigated. Results showed that the frontline users are satisfied with the usability of the e-learning platform. Overall, the informatics consultant users are satisfied with the application, although they rated the application as poor in terms of efficiency and control. The results showed that many areas where usability was problematic are related to general interface usability (GIU), and instructional design and content, some of which might account for the poorly rated aspects of usability. The findings should be of interest to developers, designers, researchers, and usability practitioners involved in development of e-learning systems.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".