Understanding the effects of message framing on physical activity action planning: A preliminary look at the role of risk perception as a moderator
Bibliographic record
Abstract
Action planning (AP) is a strategy for increasing physical activity. Persuasive messages could be useful in promoting AP and optimally framed messages could maximize effectiveness. Research suggests that message framing (i.e., presenting messages either in terms of benefits or costs associated with the behaviour), may be only minimally effective in promoting AP. However, researchers have overlooked the possible moderating role of individuals' risk perceptions regarding AP. The purpose of this study was to examine risk perception as a moderator of the effect of framed messages on AP. Preliminary data were gathered online from 88 inactive adults who read either a gain- or loss-framed AP message. Data were collected regarding perceived risks of AP (pre-message) and AP (post-message). Logistic regression analyses were conducted to examine perceived risk of AP as a moderator of framed messaging effects on AP. A medium to large interaction effect between the message frame and risk perception (emotional risk) approached significance [OR = 3.442, 95% confidence intervals (CI): 0.927, 12.778]. Post hoc analyses indicated that individuals with greater emotional risk perceptions (e.g., worried they could not stick to their plan) were 1.95 (95% CI: 0.718, 5.294] times more likely to AP when presented a gain-framed message. Individuals presented with the loss-framed message decreased their likelihood of AP when their emotional risk increased [OR = 0.563, 95% CI: 0.238, 1.331]. This study provides preliminary evidence that perceived risk may moderate the effect of framed messages on AP. Data are being collected from a larger sample to further explore this hypothesis.
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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 | 0.015 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".