The Effects of Gain- versus Loss-Framed Messages Following Health Risk Information on Physical Activity in Individuals With Multiple Sclerosis
Bibliographic record
Abstract
Few people with multiple sclerosis engage in physical activity. Messaging interventions may motivate more physical activity among these individuals. The purpose of this online study was to evaluate an intervention presenting participants with multiple sclerosis (N = 237) with risk information (i.e., information demonstrating people with multiple sclerosis are more likely to experience certain health issues) or no risk information followed by gain- or loss-framed physical activity messages. Participants completed questionnaires on Days 1, 6, and 28 and received information material on Days 2-5. The dependent variables were as follows: physical activity intentions and behavior, response and task efficacy, perceived threat (i.e., perception of threat to health issues relevant to people with multiple sclerosis), and avoidance (i.e., avoiding thinking about/doing something about the health issues presented in the messages). Analyses indicated physical activity and response efficacy increased over time. Also, participants receiving risk information had higher levels of physical activity and perceived threat. However, manipulation checks showed no differences between participants regarding perceptions of risk information or gain/loss-framed messages. Despite the lack of impact of the framing intervention, this study suggests that a brief informational intervention can positively influence physical activity and certain correlates of physical activity among people with multiple sclerosis.
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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 | 0.007 |
| 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 | 0.000 | 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".