Review and Evaluation of Online Tobacco Dependence Treatment Training Programs for Health Care Practitioners
Bibliographic record
Abstract
BACKGROUND: Training health care professionals is associated with increased capacity to deliver evidence-based smoking cessation interventions and increased quit rates among their patients. Online training programs hold promise to provide training but questions remain regarding the quality and usability of available programs. OBJECTIVE: The aim was to assess the quality of English-language online courses in tobacco dependence treatment using a validated instrument. METHODS: An environmental scan was conducted using the Google search engine to identify available online tobacco dependence treatment courses. The identified courses were then evaluated using the Peer Review Rubric for Online Learning, which was selected based on its ability to evaluate instructional design. It also has clear and concise criteria descriptions to ensure uniformity of evaluations by trained experts. RESULTS: A total of 39 courses were identified, of which 24 unique courses were assessed based on their accessibility and functionality during the period of evaluation. Overall, the course ratings indicated that 17 of 24 courses evaluated failed to meet minimal quality standards and none of the courses evaluated could be ranked as superior. However, many excelled in providing effective navigation, course rationale, and content. Many were weak in the use of instructional design elements, such as teaching effectiveness, learning strategies, instructor's role, and assessment and evaluation. Evaluation results and suggestions for improvement were shared with course administrators. CONCLUSIONS: Based on the courses evaluated in this review, course developers are encouraged to employ best practices in instructional design, such as cohesiveness of material, linearity of design, practice exercises, problem solving, and ongoing evaluation to improve existing courses and in the design of new online learning opportunities.
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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 | 0.026 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".