Distinctive features of goal conflict as a barrier to exercise
Bibliographic record
Abstract
Aims of this study were to establish unique effects of goal conflict on exercise, beyond those of initial motivation, and to test the hypothesis that goal conflict differentiates individuals' responses to exercise promotion using positive and negative message frames. A new measure of goal conflict was administered to undergraduates in a preliminary survey. Of these respondents, 150 with high exercise motivation participated in this study. Participants estimated their past-week physical activity. Following random assignment, they evaluated a positively or negatively framed exercise-promotion brochure. They then completed motivational measures of their attitudes, subjective norms, perceived behavioural control, and intentions to exercise. Two weeks later, participants again estimated their past-week physical activity. Participants' frequency and duration of vigorous activity (at follow-up) were negatively related to goal conflict after controlling for the motivational measures. Participants' frequency of vigorous and moderate exercise increased significantly from baseline to follow-up, after exposure to either message frame. The increase in moderate activity was due to participants with low goal conflict who received the positive frame, whereas the increase in vigorous activity was due to those with high goal conflict who received the negative message frame. In sum, goal conflict predicts less frequently engaging in exercise, even among individuals who are highly motivated and equated on supporting beliefs and intentions. Although positively framed messages are generally advised for promoting exercise, high goal conflict predisposes individuals to respond favorably to messages that are negatively framed.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".