Using Social Learning Theory in a Tobacco Prevention Program for Youth
Bibliographic record
Abstract
Youth based school centered harm minimization programs for tobacco uptake prevention is congruent with the well-known social cognitive theory (Bandura, 1997) which seeks to build skills and confidence for change while modeling successful change processes noted in others. One harm reduction initiative, The Academy for Tobacco Prevention has been implemented. This free access, online program supports and aligns with the Alberta Health and Wellness education curriculum. The tandem goals for this research study were [1] evaluate how effectively the Academy for Tobacco Prevention shifts knowledge and usage of tobacco related products with youth and [2] to apply Bandura’s social learning theory model in identifying personal and environmental factors that can predict youth’s health-promoting self-care behaviors and resistance to peer pressure. This presentation will provide an overview of the dynamic and engaging online modules included in the Academy for Tobacco Prevention, an overview of the logic model used for program evaluation, and a summary of Bandura’s social learning theory relative to individual behavior interactions among personal factors, environmental factors and behavior changes. The presentation will also include of synopsis of the quantitative data accumulated that demonstrates why this is a successful tobacco prevention model for harm reduction.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".