The Association Between Physical Self-Discrepancies and Women’s Physical Activity: The Mediating Role of Motivation
Bibliographic record
Abstract
The objectives of this study were to test the associations between physical self-discrepancies (actual:ideal and actual:ought) and physical activity behavior, and to examine whether motivational regulations mediate these associations using self-discrepancy (Higgins, 1987) and organismic integration (Deci & Ryan, 1985) theories as guiding frameworks. Young women (N = 205; M(age) = 18.87 years, SD = 1.83) completed self-report questionnaires. Main analyses involved path analysis using a polynomial regression approach, estimation of direct and indirect effects, and evaluation of response surface values. Agreement between actual and ideal (or ought) physical self-perceptions was related to physical activity both directly and indirectly as mediated by the motivational regulations (R(2) = .24-.30). Specifically, when actual and ideal self-perceptions scores were similar, physical activity levels increased as actual and ideal scores increased. Furthermore, physical activity levels were lower when the discrepancy was such that ideal or ought self were higher than actual self. These findings provide support for integrating self-discrepancy and organismic integration theories to advance research in this area.
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 | 0.002 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".