Bibliographic record
Abstract
Background. Physical activity has been shown to decrease the risk of a variety of diseases. However, recent studies indicate that only 15% of Canadian adults engage in adequate levels of physical activity. As such, an area of interest for physical activity promotion has been the use of persuasive messages, specifically, the use of framing effects as a method of persuasive communication. This study uses the Self-Determination Theory (SDT) to investigate the effects of framed health messages on autonomous motivation. Methods. 107 York University undergraduate students (N=107; 51 females, 56 males) ages 18 – 30 were recruited from the school of Kinesiology and Health Sciences. Participants were randomly assigned to one of three message groups: gain-framed, loss-framed and control. They were given and instructed to read the messages. Afterwards, the participants’ autonomous motivation levels were measured. Results. 68.2% of the participants were considered physically active. No significant difference in autonomous regulation levels were observed between the three frame groups. However, a significant interaction was shown between participants’ gender and frame condition; among the female participants, levels of autonomous regulation were significantly higher in the loss frame group, when compared to the control group. Conclusion. Based on the results of this study, women who were exposed to loss-framed messages tended to demonstrate higher levels of autonomy. Similar framing effects were not evident in males.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.006 |
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".