Exploring the efficacy of affective mental contrasting versus goal setting on physical activity and psychological wellbeing in first year university students: An experimental study
Bibliographic record
Abstract
Students transitioning to university face many challenges, including decreased physical activity and psychological wellbeing. Mental contrasting, a self-regulation strategy that involves setting a goal, then associating prospective positive outcomes with present obstacles related to that goal, may be effective to boost physical activity and wellbeing. Research shows that mental contrasting may be particularly effective when framed with affective messaging (e.g., exercise is enjoyable). The purpose of this study was to examine the efficacy of 4 weeks of affective mental contrasting, compared to goal setting alone, on physical activity and psychological wellbeing among first year university students. In a randomized controlled trial over a 4-week period, 114 first year university students were guided through either goal setting alone, or goal setting plus affective mental contrasting, focused on physical activity. Physical activity and psychological wellbeing were measured via questionnaire at baseline and 4 weeks. Participants in both groups significantly increased their physical activity, F (1, 85) = 50.13, p < .001, partial eta squared = .37 and psychological wellbeing, F (1, 82) = 5.14, p < .05, partial eta squared = .06, but between-group differences were not significant. An exploratory analysis showed that among participants who were active for more than 60 minutes per week at baseline, affective mental contrasting was more effective than goal setting alone at increasing physical activity, F (1, 54) = 4.73, p = .034, partial eta squared = .08. Both goal setting and affective mental contrasting are effective strategies to increase physical activity among university students.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".