Do you want the good news or the bad news? The effects of gain- versus loss-framed messages on health and physical activity beliefs and cognitions
Bibliographic record
Abstract
Prospect theory suggests that the effectiveness of health messages varies depending on the emphasis on benefits of adopting a health behaviour (i.e., gain-framed) versus risks of not adopting a behaviour (i.e., loss-framed). Gain-framed messages are thought to be more effective (vs. loss-framed) for persuading health-prevention behaviours such as physical activity (PA). Guided by protection motivation theory, this study examined the effects of PA messages targeting people with spinal cord injury (SCI). Gain-framed messages were hypothesized to be more effective than loss-framed. People with SCI (N=96) were randomized to receive control, gain-framed, or loss-framed messages targeting health and PA. Perceived health risk, response efficacy, intentions, and PA were measured pre- and 24hr-post message. A series of 2(time) x 3(frame) repeated-measures ANOVAs indicated time x frame interactions (partial-eta2>.01). Post-hoc analyses indicated significant changes in perceived health risk for loss- and gain-framed conditions (t
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".