“I gained a skill and a change in attitude”: A Case Study Describing How an Online Continuing Professional Education Course for Pharmacists Supported Achievement of Its Transfer-to-Practice Outcomes
Bibliographic record
Abstract
The convenience and flexibility of online learning clearly make it an attractive option for learners in professional development contexts. There is less clarity, however, about how it fares as a vehicle for enabling the applied, practice-oriented outcomes typically associated with professional development learning.This paper presents a case study describing how transfer-of-learning strategies were employed in a continuing professional education (CPE) course developed for practicing pharmacists, called ADAPT (ADapting pharmacists’ skills and Approaches to maximize Patients’ drug Therapy effectiveness).To gain insight into the extent to which learning was transferred to practice as a result of participation in the course, qualitative data were collected over a 12-month period from participants of the 2010 pilot offering of ADAPT. Participants reported making changes to their practice as a result of participating in the course, and they identi- fied three course features as being particu- larly useful in facilitating practice transfer: providing learners with (i) a vision of targeted outcomes and skills, (ii) support to enable them to attain targeted outcomes and skills, and (iii) explicit preparation for action.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.008 | 0.010 |
| 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".