Application of the RE-AIM Framework to Evaluate the Impact of a Worksite-Based Financial Incentive Intervention for Smoking Cessation
Bibliographic record
Abstract
OBJECTIVE: To apply the RE-AIM framework to examine factors that may have influenced the impact of a financial incentive smoking cessation intervention delivered at General Electric (GE) worksites. METHODS: Intervention reach and efficacy were examined alone and in combination across worksites. Telephone interviews were conducted with worksite staff to explore organizational-level factors that may have influenced program adoption, implementation, and maintenance. Focus groups were conducted with employees to explore barriers and facilitators to program participation. RESULTS: Intervention impact varied considerably across GE business industries when reach and efficacy both were examined instead of efficacy alone. Barriers that may have hindered program success include time constraints, competing priorities, work stress, and the lack of public visibility. CONCLUSION: Employers considering financial incentive interventions for smoking cessation should examine how organizational context and real-world constraints may influence differential impact across sites.
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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 | 0.066 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".