Trait procrastination undermines outcome and efficacy expectancies for achieving health-related possible selves
Bibliographic record
Abstract
Abstract People often fail at following through with their health behaviour goals. How health goals are cognitively represented holds promise for understanding successful health behaviour change. Health-related possible selves (HPS) reflect cognitive representations of a future self that people may wish to achieve (hoped-for-HPS) or avoid (feared-HPS), that can promote health behaviour change. However, success depends on the strength of the efficacy and outcome expectancies for achieving/avoiding the HPS. Personality traits linked to poor self-regulation are often not considered when assessing the potential self-regulatory functions of HPS. The current study addressed this issue by examining the associations of trait procrastination with efficacy and outcome expectancies for hoped-for-HPS and feared-HPS, and health behaviour change intentions and motivations in a community sample (N = 191) intending to make healthy changes in the next 6 months. Trait procrastination was associated with weaker intentions and motivations for health behaviour change, and lower efficacy and outcome expectancies for hoped-for-HPS, but not feared-HPS. Bootstrapped multiple mediation analysis found significant indirect effects of procrastination on health behaviour intentions, through outcome, but not efficacy, expectancies for hoped-for-HPS. Results suggest that issues in imagining a hoped-for-HPS can be achieved are linked to weak intentions for health behaviour change for those with chronic self-regulation difficulties. Research into interventions that strengthen feeling connected to hoped-for-HPS is recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".