The feasibility of e‐learning as a quality improvement tool
Bibliographic record
Abstract
RATIONAL, AIMS AND OBJECTIVES: Many quality problems exist in health care. We aim to investigate the feasibility and acceptability of using e-learning (defined as computer-based learning modules) to address gaps in quality of care. METHODS: We performed a qualitative evaluation of participants in a pilot e-learning program. Physician members of six medical teaching units (MTUs) at a multi-site tertiary care teaching hospital were asked to complete two e-learning modules addressing hand hygiene practices and management of community-acquired pneumonia (CAP). An e-learning design team created online modules that were made available to members of the six MTUs for 4 weeks using a password secured website. Use of the modules was voluntary. Participants' perceptions of module content, mode of delivery, and suggestions for improvement were determined through focus groups. We then performed content analysis on the transcripts. We used system data to define patterns of module access. RESULTS: Out of 55 eligible users, 30 (55%) logged onto the system at least once. Residents (14/30, 47%) were less likely to use the system than medical students (9/14, 64%) and attending staff (7/11, 64%). Learners at all levels thought the modules were easy to use. Participants liked the knowledge-based material in the CAP module because it directly applied to their work. There were less favourable opinions of the hand hygiene module CONCLUSIONS: Generating e-learning modules targeted at gaps in quality of care is feasible and acceptable to learners. Future studies should assess whether these approaches lead to desired changes in behavior.
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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 | 0.088 | 0.161 |
| 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 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".