Development and evaluation of an online training program based on the O-AMAS teaching model for community pharmacists in the post-COVID-19 era
Bibliographic record
Abstract
Background Formerly, the community pharmacists' work was mainly focused on drug supply. However, during the COVID-19 epidemic outbreak, community pharmacists in Wuhan played an important role in control and prevention of SARS-CoV-2 and in providing pharmaceutical care. Due to a lack of adequate knowledge and skills, many community pharmacists were not able to cope with healthcare work timely and efficiently. To improve community pharmacists' specialized knowledge and enhance their professional competence through systemic training in the post-COVID-19 era. Methods Based on the O-AMAS (Objective, Activation, Multi-learning, Assessment and Summary) teaching model and flipped classroom, an online continuing training program containing four sections was developed. It was a semi-experimental study with no control group. Quantitative tests before and after training as well as questionnaire were used to evaluate the outcome of this training program for community pharmacists. Results A total of twenty-six community pharmacists were invited to participate in continuing education, and twenty-five trainees finished this training program with a completion rate of 96.2 %. Quantitative tests before and after training and anonymous questionnaires were carried out to comprehensively evaluate the outcomes of this training program. Compared with the test scores before training (61.6 ± 6.6), the score after training was statistically higher, reaching 80.9 ± 7.5 ( P < 0.001). Twenty-three questionnaires were received (returns ratio, 92.0%). Notably, most of the pharmacists were satisfied with the training program. The percentage of positive responses for each item in this anonymous questionnaire was more than 85 %. Conclusion It was suggested that the O-AMAS model and the flipped classroom-based continuing educational program achieved the expected training effects. It is a promising on-the-job training approach for pharmacy continuing education. Moreover, our study also demonstrated that online learning had advantages of no geographic constraints, flexible learning beyond time and easy interaction, over traditional face-to-face training style, especially in the post-pandemic era.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".