Social support, self‐esteem, and sense of mastery as mediators of the relationships among physically active leisure, stress and health
Bibliographic record
Abstract
In this article we report results of analyses that focused on examining (a) how physically active leisure influences relationships between stress and health, and (b) how social and psychological resources (i.e., social support, self‐esteem, and sense of mastery) impact on these relationships. Data from the Canadian 1994 National Population Health Survey (NPHS) were used for the analyses. Evidence for various types of health benefits of physically active leisure were found with the use of Structural equation modeling (SEM). For the whole sample, physically active leisure was directly and positively associated with physical health and well‐being, and directly and negatively associated with mental ill‐health. Also, social support, self‐esteem, and sense of mastery mediated the impact of physically active leisure on the stress‐health relationship. Specifically, physically active leisure appeared to indirectly facilitate stress reduction through its positive effects on these social and psychological resources, which consequently, had a positive effect on health and well‐being. With respect to sex and age differences, the findings appear to suggest that women may gain more mental health benefits from physically active leisure than men, and involvement in physically active leisure may be more beneficial for health among older individuals than younger individuals. Little evidence was found for the buffer effects of physically active leisure on stress‐health associations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".