Do you want the good news or the bad news? Gain- versus loss-framed messages following health risk information: The effects on leisure time physical activity beliefs and cognitions.
Bibliographic record
Abstract
OBJECTIVE: The primary purpose was to examine the relative effectiveness of chronic disease and psychological health risk information combined with gain- versus loss-framed leisure time physical activity (LTPA) messages for changing perceived personal risk, LTPA response efficacy (i.e., the belief that LTPA can effectively reduce risk), and LTPA intentions. A secondary purpose was to explore the relationship between message framing and cognitive processing. METHOD: Baseline assessments of perceived risk for inactivity-related disease and psychological health problems, LTPA response efficacy, and intentions were measured among 96 individuals with spinal cord injury (SCI). Participants read population-specific information about the risk for inactivity-related disease and psychological health problems following SCI, and perceived risk was reassessed. Participants were then randomized to read LTPA response efficacy messages emphasizing the benefits of LTPA (gain framed) or the risks of inactivity (loss framed). Immediately following message exposure, cognitive processing (i.e., thought listing and message recall), LTPA response efficacy, and LTPA intentions were assessed. RESULTS: Changes in perceived risk were observed following exposure to health risk information. Changes in LTPA response efficacy and intentions were greater following loss-framed messages targeting psychological health compared with gain-framed messages. Greater cognitive processing was observed following loss-framed messages compared with gain-framed messages. CONCLUSION: Following exposure to psychological health risk information, loss-framed messages may be more effective than gain-framed messages for eliciting cognitive processing and changing LTPA beliefs and intentions.
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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 | 0.011 |
| 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 | 0.001 |
| 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".