Participation in online continuing education
Bibliographic record
Abstract
OBJECTIVES: The ADAPT (ADapting pharmacists' skills and Approaches to maximize Patients' drug Therapy effectiveness) e-learning programme requires weekly participation in module activities and facilitated discussion to support skill uptake. In this study, we sought to describe the extent and pattern of, satisfaction with and factors affecting participation in the initial programme offering and reasons for withdrawal. METHODS: Mixed methods - convergent parallel approach. Participation was examined in qualitative data from discussion boards, assignments and action plans. Learner estimations of time commitment and action plan submission rates were calculated. Surveys (Likert scale and open-ended questions) included mid-point and final, exit and participation surveys. KEY FINDINGS: Eleven of 86 learners withdrew, most due to time constraints (eight completed an exit survey; seven said they would take ADAPT again). Thirty-five of 75 remaining learners completed a participation survey. Although 50-60% of the remaining 75 learners actively continued participating, only 15/35 respondents felt satisfied with their own participation. Learners spent 3-5 h/week (average) on module activities. Factors challenging participation included difficulty with technology, managing time and group work. Factors facilitating participation included willingness to learn (content of high interest) and supportive work environment. Being informed of programme time scheduling in advance was identified as a way to enhance participation. CONCLUSIONS: This study determined extent of learner participation in an online pharmacist continuing education programme and identified factors influencing participation. Interactions between learners and the online interface, content and with other learners are important considerations for designing online education programmes. Recommendations for programme changes were incorporated following this evaluation to facilitate participation.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.005 |
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".