Ease of imagination, message framing, and physical activity messages
Bibliographic record
Abstract
OBJECTIVES: The purpose of this research was to replicate a study that examined how message framing and ease of imagination interact to influence attitudes towards the prevention of heart disease through physical activity and a healthy diet. Changes were made such that only physical activity behaviour was profiled and assessed as a moderating variable. It was hypothesized that gain-framed messages would positively influence attitudes with hard to imagine symptoms, that loss-framed messages would positively influence attitudes with easy to imagine symptoms and exercise frequency would moderate the findings. DESIGN: This study employed a 2 (easy or hard to imagine symptoms) by 2 (gain- or loss-framed) Solomon square design whereby participants, half of whom completed a pre-test, were randomly assigned to one of four conditions: easy to imagine/gain-framed, hard to imagine/gain-framed, easy to imagine/loss-framed, or hard to imagine/loss-framed. METHODS: Participants included adults over the age of 55 years (N=57) and undergraduate students (18-22 years; N=118). They were described either hard to imagine or easy to imagine symptoms of heart disease and diabetes and asked to imagine them. Participants then read either a gain- or loss-framed physical activity message followed by post-test questionnaires that assessed attitudes, exercise frequency, and demographics. RESULTS: Regression analyses showed no significant framing effects but significant effects for ease of imagination and exercise frequency as a moderating variable. CONCLUSIONS: This study failed to replicate the original research findings but showed that participants who exercised the least and were in the hard to imagine condition had the worst attitudes towards physical activity.
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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 | 0.005 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".