Sport for Recovery from Psychiatric Disorders: Psychosocial Outcomes and Factors Contributing Subjective Well-Being
Bibliographic record
Abstract
Sport has gained attention as a potentially useful intervention for recovery and well-being among people with psychiatric disorders. However, there is existing concern among practitioners whether sport exacerbates clinical outcomes. The purpose of the current study was to examine whether and how participation in sport is associated with positive psychosocial outcomes. This study focused on football activities that has been organized mainly for people with psychiatric disorders in Japan, called Social Football, and a total of 88 players participated in an online survey. Participants were asked to compare the status of medication amount, sleep quality, speed of thought, and engagement in social activity between now and before they began playing Social Football for the first time. Additionally, subjective well-being, athletic identity, serious leisure, activity enjoyment, perceived social connectedness through Social Football, depressive symptoms, and loneliness were measured. The participants generally perceived reduced medication amount, better sleep quality, faster speed of thought, and increased engagement in social activity since they began engaging in sport. In addition, results indicated that perceived social connectedness in sport and loneliness were significant predictors of subjective well-being after other variables were statistically controlled. While the results must be interpreted with caution due to the potential for selection bias, the results may suggest that facilitating greater opportunities to establish social relationships with others is important for players’ subjective well-being.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".