Using Evaluation Capacity Building (ECB) to Interpret Evaluation Strategy and Practice in the United States National Tobacco Control Program (NTCP): A Preliminary Study
Bibliographic record
Abstract
Abstract: The Office on Smoking and Health (OSH) of the Centers for Disease Control and Prevention (CDC) supports state programs for the prevention and control of tobacco use through the National Tobacco Control Program (NTCP). OSH provides the NTCP with expert guidance and technical assistance on tobacco use control and disease surveillance as well as evaluation of tobacco control programs. These services fit national health goals and provide data to inform national and state policy making and program planning. However, the NTCP’s delivery of services, achievement of goals, and evaluation of efforts is hindered by fluctuations in dedicated state funds to support tobacco use prevention and control programs. To maximize effort and resources, evaluation capacity building (ECB) is a strategy for strengthening evaluation services, program efficiency, and program effectiveness, that is, program improvement. This article interprets NTCP using an ECB frame to learn the utility of this approach for making suggestions for structural and practice changes that lead to program improvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.108 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".