Imagining the Possibilities: The Effects of a Possible Selves Intervention on Self-Regulatory Efficacy and Exercise Behavior
Bibliographic record
Abstract
This experiment examined the effects of a possible selves intervention on self-regulatory efficacy and exercise behavior among 19 men and 61 women (M age = 21.43 years, SD = 3.28) who reported exercising fewer than 3 times per week. Participants were randomly assigned to a control condition, a hoped-for possible selves intervention condition, or a feared possible selves intervention condition. The hoped-for and feared possible selves interventions required participants to imagine themselves in the future as either healthy, regular exercisers or as unhealthy, inactive individuals, respectively. Participants in the control condition completed a quiz about physical activity. Measures of self-regulatory efficacy (scheduling, planning, goal setting, and barrier self-efficacy) were taken immediately before and after the intervention. Participants who received either possible selves intervention reported greater exercise behavior 4 weeks and 8 weeks postintervention than participants in the control group. Planning self-efficacy partially mediated the effects of the possible selves intervention on exercise behavior over the first 4 weeks of the study. These findings highlight the effectiveness of possible selves interventions for increasing exercise behavior and the role of self-regulatory processes for explaining such effects.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".