Women's self-compassion and exercise motivations
Bibliographic record
Abstract
To gain a further understanding of the role of self-compassion in women's exercise motivations the purpose of this research was to examine the relationship between self-compassion and women's exercise motivations. Participants were 72 women exercisers between 18 and 40 years (M = 24.18, SD = 5.83). The women completed an online questionnaire that included a demographic survey, the Behavioural Regulation in Exercise Questionnaire, the Rosenberg Self-Esteem Scale, the Self-Compassion Scale, the Self- determination Scale, the modified Drive for Muscularity Attitudes Questionnaire, the Drive for Thinness Scale, and the Body Appreciation Scale. The results showed that self-compassion was negatively correlated with introjected motivation (r= -.50, p <.05), drive for muscularity (r= -.30, p <.05), and drive for thinness (r = -.54, p <.05); and that self-compassion was positively correlated with body appreciation (r= .73, p <.05) and self-determined motives to exercise (r= .53, p <.05). Follow-up hierarchical regression analyses showed that self-compassion predicted unique variance beyond self-esteem on introjected motivation (R2 = .25, p <.05; ?R2 = .06, p <.05), drive for thinness (R2 = .29, p <.05; ?R2 = .05, p <.05), and self-determined motives to exercise (R2 = .28, p <.05; ?R2 = .05, p <.05). The current study highlights that self-compassion might play a role in the promotion of adaptive, healthy, and positive exercise motivations for women exercisers.
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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 | 0.004 |
| 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 | 0.000 |
| 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".