The relationship between implicit and explicit believability of exercise-related messages and intentions.
Bibliographic record
Abstract
OBJECTIVE: This research explored whether implicit or explicit believability of exercise advertising predicted attitudes and intentions. It was hypothesized that implicit believability would be a stronger predictor of attitudes than explicit believability and that implicit believability would predict intentions. METHOD: Undergraduate student participants (N = 306) viewed health promotion or appearance-based exercise-related advertisements. They completed an implicit believability task followed by questionnaires of issue involvement, attention paid to the advertisements, explicit believability, exercise attitudes, and intentions to exercise. Participants listed 5 thoughts they had when viewing the advertisements. Health and appearance models were tested using structural equation modeling. Thoughts were coded and valence (negative statements subtracted from positive), believability, and motivation indices were created. Correlations between indices and model variables were calculated. RESULTS: Both models were good fits of the data. In the health condition, explicit believability did not predict attitudes or intentions but implicit believability predicted attitudes and explicit believability. In the appearance condition, implicit believability was negatively related to intentions, but was not related to explicit believability or attitudes. There were small positive correlations between attitudes and the thought-listing valence index in both conditions. CONCLUSIONS: The results indicate that exercise-related health promotion messages are believable and that the initial reaction to them coincides with reflective attitudes. However, if appearance messages are believed (even if not explicitly), the effects may be detrimental. It is important to include implicit measures in messaging research as they allow for a more complete understanding of how health messages may influence related cognitions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".