Comprehensive evaluation of an online tobacco control continuing education course in Canada
Bibliographic record
Abstract
INTRODUCTION: To respond to the increasing need to build capacity for planning, implementing, and supporting tobacco control strategies, an evidence-based, online continuing education (CE) course aimed at Canadian public health professionals was developed. The purpose of this study was to comprehensively evaluate the course, Tobacco and Public Health: From Theory to Practice (http://tobaccocourse.otru.org). METHODS: Rossett and McDonald's revision of Kirkpatrick's four-level evaluation model for training programs guided the evaluation design. A pre-, post-, and follow-up single group design assessed immediate reactions to course modules, knowledge change and retention, practice change, and overall perceived value of the course. Six external peer reviewers evaluated course module content. RESULTS: Fifty-nine participants completed all three course modules and the final online questionnaire at time 3, representing a response rate of 78%. Significant knowledge gains occurred between times 1 and 2 (p < 0.001). Although time 3 scores remained higher than time 1 scores for each module (p < 0.001), they decreased significantly between times 2 and 3 (p < 0.001). The majority of participants (93%) felt the topics covered were useful to their daily work. All but one participant felt the course was a good investment of their time, and nearly all participants (97%) stated they would recommend the course to others. Peer reviewers found that module content flowed well and was comprehensive. DISCUSSION: This comprehensive evaluation was valuable both for assessing whether course goals were achieved and for identifying areas for course improvement. We expect this design would be a useful model to evaluate other online continuing education courses.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".