The effects of gain- versus loss-framed messages following health risk information on physical activity in individuals with multiple sclerosis
Bibliographic record
Abstract
Some secondary complications of multiple sclerosis (MS) can be countered by physical activity (PA) participation. To encourage PA participation, practitioners may deliver risk information about secondary complications of MS proceeded by messages encouraging PA participation. This study examined the effects of risk information (or no risk information) followed by gain- or loss-framed messages on perceived risk of secondary complications, fear arousal, and PA intentions, behaviour, and self-efficacy in people with MS. Two-hundred sixty-two participants (M age = 41.62, SD = 9.47) completed measures on Day 1, Days 2-5, and Days 6, 14, and 28. Participants read corresponding risk information and/or framed PA messages on Days 2-5 (one health topic per day), covering health topics relevant to people with MS (i.e., chronic diseases, falls, fatigue, and mental health). Repeated measures ANCOVAs showed risk information participants had higher scores than no risk information participants for PA and perceived risk (ps < .04). ANCOVAs revealed that participants who read information about chronic diseases had higher scores for intentions than participants who did not read risk information (p = .04). Participants who received risk information/gain-framed messages had higher fear arousal regarding chronic diseases, falls, and mental health compared to no risk information/gain-framed participants (ps < .02) and higher fear arousal regarding fatigue compared to no risk information/gain-framed (p < .001) and risk information/loss-framed (p = .002) participants. In summary, the results suggest the provision of risk information—and in some instances gain-framed messages—effectively changes PA and potential antecedents in people with MS.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".