Managing exercise with another highly valued and conflicting leisure time goal
Bibliographic record
Abstract
Research has identified concurrent self‐regulatory efficacy as a consistent exercise predictor when adults pursue another non‐exercise leisure time goal. Although intergoal conflict is an inconsistent exercise predictor, prior research did not ensure that goals were sufficiently highly valued to truly conflict. Other possible exercise predictors have not been examined among concurrent goals. The purpose was to examine whether intergoal conflict and outcome expectations (likelihood; value) predicted moderate‐vigorous exercise over 1 month, beyond concurrent self‐regulatory efficacy, when adults held highly valued, conflicting exercise and non‐exercise goals concurrently. Eighty‐seven adult exercisers pursuing highly valued and conflicting exercise and non‐exercise goals completed online surveys assessing (1) concurrent self‐regulatory efficacy, intergoal conflict, and outcome expectations at Time 1 and (2) exercise over the prior month at Time 2. A hierarchical multiple regression (R2 adjusted = 0.24, p < .001) revealed intergoal conflict and outcome expectations accounted for a significant additional 13% of exercise variance, beyond self‐regulatory efficacy. Future research should examine these social cognitions across adults who vary in their exercise levels (i.e., beginner, irregular, regular exercisers). Valuable information about which social cognitions should be targeted to improve exercise levels among each group to that of regular exercisers would result.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".