Promoting exercise behaviour: An integration of persuasion theories and the theory of planned behaviour
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to examine the effectiveness of a theoretical integrated persuasive message on exercise motivation in college students. METHOD: Four hundred and fifty introductory psychology students (M age = 20.02 years; SD = 3.94) were randomly assigned to reading positively or negatively framed strong messages advocating exercise. The messages were attributed to a credible source, a non-credible source or to a no-source control condition. Theory of planned behaviour (TPB) constructs (i.e. attitude, subjective norm, perceived behavioural control) and cognitive responses (i.e. thought listing) were measured immediately and 2 weeks following the delivery of the intervention. RESULTS: Unfortunately, the results did not corroborate previous research, as we did not find any significant effects between experimental groups on any psychological or behavioural variable. CONCLUSIONS: There may be several potential explanations for the lack of effects, including the interaction between the type of persuasive information (TPB implications) and sources of persuasion and how these persuasive messages are processed (elaboration likelihood model/cognitive response implications). The theoretical implications of this research are discussed with a view towards future directions for exercise promotion initiatives using theoretically driven interventions.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".