Persuading Students to Exercise: What Is the Best Way to Frame Messages for Normal-Weight Versus Overweight/Obese University Students?
Bibliographic record
Abstract
OBJECTIVE: The authors investigated the effect of gain-framed (GF) compared with loss-framed (LF) messages on exercise behaviors in normal weight and among overweight/class I obese. The authors also examined which groups would have significantly improved exercise behaviors over time. PARTICIPANTS: Sixty-four undergraduates were randomized to the 4 groups by message type and weight category from September 2008 to December 2011. METHODS: After screening, students received messages and attended an exercise instruction session. RESULTS: There were no significant differences between GF or LF message groups among normal weight or overweight/obese on the primary outcomes at posttest. After receiving the GF messages, the overweight/obese group was the only group to have a significant increase on all 3 primary outcomes: fitness center attendance (p = .038), combined moderate- and vigorous-intensity activity (p = .005), and strength training (p = .037). CONCLUSIONS: The exercise behaviors of undergraduate students who are overweight or obese can benefit from GF messages.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".