"I'm inactive, but I'm still a good person": The effect of self-affirmation on responses to gain and loss framed physical activity messages
Bibliographic record
Abstract
Physical activity (PA) information may threaten self-integrity for inactive people because it calls into question their ability to control their health. When threatened, people's ability to process information may be compromised. Further, they may downplay threats to restore self-integrity. Self-affirmation (SA) is the process of affirming oneself on core values. Research shows that pairing SA with health information can improve responses to health messages. Few researchers have examined SA in a PA context, and none have examined SA and the nature of PA messages, nor implicit responses. This research examined whether SA influenced reactions to gain and loss framed PA messages among 155 (Mage = 22.51, SD = 7.23) inactive people. Participants were randomized to receive either a SA or control activity and to read a gain or loss framed PA message. They completed measures of attentional bias, psychological responding and, one week later, recalled PA. A MANCOVA showed that the gain-framed message was associated with attentional bias away from health threat words; the loss frame message was associated with attentional bias toward health threat words, F = 5.36, p = .02. There was no main SA effect nor interaction. Another MANCOVA showed that SA was associated with lower perceived threat F = 3.75, p = .050 and higher self-efficacy F = 4.28, p = .041. The loss-framed message was associated with greater perceived threat F = 7.92, p = .003. There was no interactive or main effect for follow-up PA. SA showed modest benefits independent of message frame.Acknowledgments: Funding for this project received through an University of Manitoba Reseach Grant
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.004 | 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".