Aligning Practice Data and Institution-specific CPD: Medical Quality Management as the Driver for an eLearning Development Process
Bibliographic record
Abstract
For hospital physicians, alignment of Continuing Professional Development (CPD) with quality improvement efforts is often absent or rudimentary. The purpose of this study was to evaluate a CPD development process that created accessible learning opportunities and aligned CPD with practice data. We conducted a chart audit to identify patient safety and quality of care issues within the institution, then established an eLearning approach that supported quick and cost effective development of high-quality interactive CPD opportunities. We tested a pilot module on the management of common infections in sub-acute care settings with fifteen (68%) residents and three staff physicians to evaluate the approach. One resident and three staff agreed to a follow-up interview. The satisfaction survey indicated that participants felt the content was generally appropriate and the module well designed. Significant improvements to knowledge were reported in the multi-drug resistance (Mean Difference = 25%, p = 0.002), infection management (MD = 32%, p < 0.001), and cellulitis risk factor (MD = 22%, p = 0.02) questions, as well as in the overall score (MD = 19%, p < 0.001). In terms of confidence in their answers, the mean rating pre-module was 3.17, rising significantly to 3.92 post-module (p < 0.001). In this way, collaboration between quality management and education committees allowed for the development of relevant CPD for physicians, with eLearning providing a timely and accessible way to deliver training on emerging issues.
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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 | 0.043 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".