Promoting Fruit and Vegetable Intake through Messages Tailored to Individual Differences in Regulatory Focus
Bibliographic record
Abstract
BACKGROUND: Researchers must identify strategies to optimize the persuasiveness of messages used in public education campaigns encouraging fruit and vegetable (FV) intake. PURPOSE: This study examined whether tailoring messages to individuals' regulatory focus (RF), the tendency to be motivated by promotion versus prevention goals, increased the persuasiveness of messages encouraging greater FV intake. METHOD: Participants (n = 518) completed an assessment of their RF and were randomly assigned to receive either prevention- or promotion-oriented messages. Messages were mailed 1 week, 2 months, and 3 months after the baseline interview. Follow-up assessments were conducted 1 and 4 months after the baseline assessment. RESULTS: Regression analyses revealed that at Month 4, the messages were somewhat more efficacious when congruent with participants' RF. CONCLUSION: RF may be a promising target for developing tailored messages promoting increased FV intake, and particularly for encouraging individuals to meet FV guidelines.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".