The Effects of Commercial Exercise Video Models on Women's Self-Presentational Efficacy and Exercise Task Self-Efficacy
Bibliographic record
Abstract
This experiment examined the effects of commercial exercise video models on women's self-presentational efficacy (SPE) and exercise task self-efficacy (EXSE). Participants were 101 women (M age = 20.1, SD = 1.14) who completed baseline measures of exercise status, SPE, and EXSE. One week later, they watched an exercise video featuring either “perfect-looking” exercisers whose bodies epitomized the ultra-thin, ultra-toned female cultural body ideal, or a video in which the exercisers were considered more “normal-looking.” Post-video, the SPE and EXSE measures were readministered along with a measure of exercise intentions. Controlling for baseline measures of self-efficacy, a series of 2 (model condition: perfect-looking vs. normal-looking) × 2 (participant's exercise status: regular exerciser vs. infrequent/nonexerciser) ANCOVAs indicated women who saw the perfect-looking models had lower post-test SPE regardless of their exercise status (p < .05) and that nonexercisers had lower post-test SPE after watching either type of model (p < .05). There were no effects of model type on EXSE. SPE also explained significant variance in exercise intentions (ΔR2 = .06) beyond that explained by EXSE (ΔR2 = .39). Results are discussed in terms of theoretical and practical significance.
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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 | 0.009 |
| 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) | 0.004 | 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".