Performance Change in Treating Tobacco Addiction: An Online, Interprofessional, Facilitated Continuing Education Course (TEACH) Evaluation at Moore's Level 5
Bibliographic record
Abstract
INTRODUCTION: Continuing education is essential to build capacity among health care providers (HCPs) to treat people with tobacco addiction. Online, interprofessional training programs are valuable; however, interpretation and comparison of outcomes remain challenging because of inconsistent use of evaluation frameworks. In this study, we used level 5 of Moore's evaluation framework to examine whether an online training program in intensive tobacco cessation counseling achieved sustained performance change among HCPs across multiple health disciplines. METHODS: The evaluation sample included 62 HCPs with direct clinical duties, who completed the online Training Enhancement in Applied Counseling and Health (TEACH) Core Course in 2015 and 2016. We compared self-reported changes in cessation counseling and clinical practices across eight core competencies from baseline to 6-month follow-up using McNemar's tests and descriptive analyses. RESULTS: Compared with baseline, significantly more HCPs reported providing cessation counseling at 6-month follow-up (44% versus 81%, P < .001). HCPs also reported significant increases in engagement in six of the eight core competencies. DISCUSSION: Online training in intensive tobacco cessation treatment can result in sustained performance improvement at 6 months. However, availability of resources and clinical context may influence the extent to which HCPs are able to implement their learned skills. Furthermore, continuing education programs should consider the use of consistent evaluation frameworks to promote cross program comparisons.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".