Examination of the Relationship Between Imagery Use, Efficacy Beliefs, and Body Image in Females
Bibliographic record
Abstract
Given the prevalence of inactivity among women, it is imperative to examine sources which may influence exercise behavior. Researchers have begun to examine the practical application of exercise imagery on involvement in physical activity (Giacobbi et al., 2003; Milne et al., 2008). Using the Applied Model of Imagery Use in Exercise (Munroe-Chandler & Gammage, 2005), imagery use, efficacy beliefs, and body image among female exercisers (N = 300) was investigated. Results revealed frequent use of exercise imagery, high efficacy beliefs, and positive body image cognitions among exercisers. Structural equation modeling revealed that efficacy beliefs did not mediate the relationship between imagery use and body image among a specific sample of female exercisers. However, the results do suggest that exercise imagery significantly predicts all four types of efficacy belief types (Efficacy Expectancy, Outcome Expectancy, Outcome Value, and Self-presentational Efficacy). Further examination of the suggested relationships in the applied model is needed.
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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".