Enhancing Anticoagulation Continuing Professional Development with Experiential Learning
Bibliographic record
Abstract
Objective: This study aims to determine the value of including practical experiential learning in a continuing professional development (CPD) course primarily for pharmacists. The Management of Oral Anticoagulation Therapy (MOAT) course blends self-paced online learning with experiential training in an anticoagulation clinic under expert supervision. Methods: An email survey was sent to 186 graduates of MOAT in October 2017. Participants were asked to indicate their confidence in providing anticoagulation services on a seven-point Likert scale prior to taking MOAT, after completing the self-paced online module, and after the experiential component. They were also asked to identify the most important aspect of the course and describe their rationale for this selection. Results: 125 surveys were completed for a response rate of 71.4%. Most respondents were pharmacists who had not completed advanced clinical training or a prior course in anticoagulation therapy management. Participants reported a progressive increase in their confidence in providing anticoagulation services from baseline (mean score, 2.9), after completing the online component (mean score, 5), and after the experiential training (mean score, 6.2) (p < 0.001 for all comparisons). Ninety percent of participants indicated the experiential component was the most important aspect of MOAT, reporting this best prepared them to translate their learning to professional practice in anticoagulation. Conclusions: The experiential component of a blended-learning CPD course in anticoagulation management was a highly valued complement to online case-based learning.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".