Evaluation of Web-Based Continuing Professional Development Courses: Aggregate Mixed-Methods Model
Bibliographic record
Abstract
BACKGROUND: Many continuing professional development (CPD) Web-based programs are not explicit about underlying theory and fail to demonstrate impact. OBJECTIVE: The aim of this study was to develop and apply an aggregate mixed-methods evaluation model to describe the paradigm, theoretical framework, and methodological approaches used to evaluate a CPD course in tobacco dependence treatment, the Training Enhancement in Applied Cessation Counseling and Health (TEACH) project. METHODS: We evaluated the effectiveness of the 5-week TEACH Web-based Core Course in October 2015. The model of evaluation was derived using a critical realist lens to incorporate a dimension of utilitarian to intuitionist approaches. In addition, we mapped our findings to models described by Fitzpatrick et al, Moore et al, and Kirkpatrick. We used inductive and deductive approaches for thematic analysis of qualitative feedback and dependent samples t tests for quantitative analysis. RESULTS: A total of 59 participants registered for the course, and 48/59 participants (81%) completed all course requirements. Quantitative analysis indicated that TEACH participants reported (1) high ratings (4.55/5, where 5=best/excellent) for instructional content and overall satisfaction of the course (expertise and consumer-oriented approach), (2) a significant increase (P ˂.001) in knowledge and skills (objective-oriented approach), and (3) high motivation (78.90% of participants) to change and sustain practice change (management-oriented approach). Through the intuitionist lens, inductive and deductive qualitative thematic analysis highlighted three central themes focused on (1) knowledge acquisition, (2) recommendations to enhance learning for future participants, and (3) plans for practice change in the formative assessment, and five major themes emerged from the summative assessment: (1) learning objectives, (2) interprofessional collaboration, (3) future topics of relevance, (4) overall modification, and (5) overall satisfaction. CONCLUSIONS: In the current aggregate model to evaluate CPD Web-based training, evaluators have been influenced by different paradigms, theoretical lenses, methodological approaches, and data collection methods to address and respond to different needs of stakeholders impacted by the training outcomes.
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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 | 0.195 | 0.204 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".