Application of the Embodiment Model of Positive Body Image and Physical Activity: A Longitudinal Study
Bibliographic record
Abstract
Positive body image (PBI) is a multidimensional construct, comprised of various facets including but not limited to body acceptance and body appreciation (Tylka and Wood-Barcalow, 2015b). Positive body image likely protects against negative physical and mental health and well-being (Tylka and Wood-Barcalow, 2015b). Menzel and Levine (2011) proposed that embodying physical activities (PA) contribute to PBI directly, as well as indirectly through increased embodiment and reduced self-objectification. Consistent with this model, this longitudinal study investigated if embodying PA affects PBI (body appreciation) through increased embodiment and decreased self-objectification. Two waves of data, separated by 6 months, were collected. Participants (recruited through Qualtrics) were 440 adults, 18+ years of age (range 18-85 years), of which 238 (54%) identified as male. They completed demographic information (e.g., age, gender) and measures of body appreciation, embodiment, self-objectification, and embodied PA. We investigated the total indirect effect (with bootstrapping) of two mediators (embodiment and self-objectification) on the relationship between embodying PA and body appreciation using AMOS. The direct effect of total embodying PA on body appreciation was nonsignificant. Serial mediation did occur, where embodying PA led to increases in embodiment (b = .10, [.01, .12], p = .057), decreases in self-objectification (b = .31, [.23, .39], p < .001) and increases in body appreciation (b = -.21, [-.32, -.08], p = .009). This study provides one of the first longitudinal tests of the embodiment model. Future research should experimentally test the embodiment model in a sample of men and women across the lifespan.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".