Investigating the relationships between self-compassion, physical activity and happiness within physical activity counselling
Bibliographic record
Abstract
Physical activity counselling (PAC) is an evidence-based approach that has been shown to increase physical activity and reduce depressive symptoms in female students over time (McFadden et al., 2017). Moreover a feasibility paper found that PAC led to increases in physical activity and mental health in university students (McFadden, submitted for review). However, levels of self-compassion and happiness within PAC, and their relationships with physical activity have yet to be explored. Thus, the purpose of the study is to investigate levels of self-compassion, physical activity, and happiness during PAC, as well as the relationships between self-compassion and happiness, self-compassion and physical activity, and physical activity and happiness. The study followed an experimental design involving online surveys pre- and post-intervention. Average self-compassion, physical activity, and happiness levels of thirty individuals were M = 2.52 ± 0.54 (Self-Compassion Scale; total score of 5), M = 7.13 ± 8.76 (Godin Leisure-Time Exercise Questionnaire; a score < 24 = insufficiently active), and M = 16.87 ± 0.55 (Subjective Happiness Scale; total score of 28), respectively. Preliminary results revealed a strong correlation between self-compassion and happiness pre-intervention (r = 0.536, p = 0.002), indicating that individuals entering the program that are more self-compassionate are happier. No significant relationships were found between self-compassion and physical activity and physical activity and happiness. This study will further our understanding of the relationships between key constructs such as self-compassion and happiness within PAC, which will therefore help to refine and guide its future implementation.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".