Testing the effects of message framing, kernel state, and exercise guideline adherence on exercise intentions and resolve
Bibliographic record
Abstract
OBJECTIVES: To study the effects of framed messages on exercise intention and resolve. DESIGN: Two (type of frame: gain or loss) × 2 (type of kernel state: desirable or undesirable outcome) post-test study. METHODS: Participants were recruited online and questioned about their previous exercise behaviour and their exercise risk perception. After this, they were randomly allocated to one of four messages that were different in terms of positive or negative outcomes (type of frame) and in terms of attained or avoided outcomes (type of kernel state). After reading the message, participants indicated their intention and resolve to engage in sufficient exercise. RESULTS: No effects were found for intention. For resolve, there was a significant interaction between type of frame, type of kernel state, and exercise adherence. Those who did not adhere to the exercise guideline and read the loss-framed message with attained outcomes reported significantly higher resolve than all other participants. CONCLUSIONS: This study indicates the relevance of including attained outcomes in message framing exercise interventions as well as a focus on exercise resolve. STATEMENT OF CONTRIBUTION: What is already known on this subject? Message framing is commonly used to increase exercise intentions and behaviour. Meta-analyses do not provide consistent support for this theory. Very little attention has been paid to resolve and message factors on framing effects. What does this study add? Framed messages have an effect on exercise resolve, but not on intention. Loss-framed messages with attained outcomes are most persuasive for those who do not adhere to exercise guidelines. Exercise framing studies should include behavioural resolve next to intention. .
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".