A Qualitative Evaluation of an Online Expert-Facilitated Course on Tobacco Dependence Treatment
Bibliographic record
Abstract
Qualitative evaluations of courses prove difficult due to low response rates. Online courses may permit the analysis of qualitative feedback provided by health care providers (HCPs) during and after the course is completed. This study describes the use of qualitative methods for an online continuing medical education (CME) course through the analysis of HCP feedback for the purpose of quality improvement. We used formative and summative feedback from HCPs about their self-reported experiences of completing an online expert-facilitated course on tobacco dependence treatment (the Training Enhancement in Applied Cessation Counselling and Health [TEACH] Project). Phenomenological, inductive, and deductive approaches were applied to develop themes. QSR NVivo 11 was used to analyze the themes derived from free-text comments and responses to open-ended questions. A total of 277 out of 287 participants (96.5%) completed the course evaluations and provided 690 comments focused on how to improve the program. Five themes emerged from the formative evaluations: overall quality, content, delivery method, support, and time. The majority of comments (22.6%) in the formative evaluation expressed satisfaction with overall course quality. Suggestions for improvement were mostly for course content and delivery method (20.4% and 17.8%, respectively). Five themes emerged from the summative evaluation: feedback related to learning objectives, interprofessional collaboration, future topics of relevance, overall modifications, and overall satisfaction. Comments on course content, website function, timing, and support were the identified areas for improvement. This study provides a model to evaluate the effectiveness of online educational interventions. Significantly, this constructive approach to evaluation allows CME providers to take rapid corrective action.
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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 | 0.052 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".