Integrating Theory with Education about Genetic Risk for Alcohol Use Disorder: The Effects of a Brief Online Educational Tool on Elements of the Health Belief Model
Bibliographic record
Abstract
Introduction: The utility of genetic risk information relies on the assumption that individuals will use the information to change behavior to reduce risk of developing health problems. Educational interventions designed to target elements of the Health Belief Model have shown to be effective in promoting behaviors for positive outcomes. Methods: A randomized controlled trial (RCT) was conducted in 325 college students to assess whether a brief, online educational intervention altered elements of the Health Belief Model that are known to be associated with motivations and intentions to change behavior. The RCT included a control condition, an intervention condition that received information about alcohol use disorder (AUD), and an intervention condition that received information about polygenic risk scores and AUD. We used t tests and ANOVA methods to compare differences in beliefs related to the Health Belief Model across study conditions and demographic characteristics. Results: Providing educational information did not impact worry about developing AUD, perceived susceptibility and severity of developing alcohol problems, or perceived benefits and barriers of risk-reducing actions. Individuals in the condition that received educational information about polygenic risk scores and AUD reported higher perceived chance of developing AUD than individuals in the control condition (adj. p < 0.01). Sex, race/ethnicity, family history, and drinking status were associated with several components of the Health Belief Model. Conclusion: Findings from this study demonstrate the need to better design and refine the educational information intended to accompany the return of genetic feedback for AUD to better promote risk-reducing behaviors.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".