Strength-Based Therapy: Empowering Athletes' Self-Efficacy and Life Satisfaction
Bibliographic record
Abstract
While it is known that focusing on positive attributes and capabilities can enhance psychological resilience, the specific effectiveness of strength-based therapy in improving self-efficacy and life satisfaction among athletes remains unexplored. By emphasizing positive attributes and capabilities, strength-based therapy aims to enhance athletes' psychological resilience and overall well-being, which are crucial in the highly competitive and physically demanding world of sports. Therefore, this study investigates the effectiveness of strength-based therapy in enhancing the self-efficacy and life satisfaction of athletes. The study used a quasi-experimental design with 50 competitive athletes aged 18-35. The methodology included an 8-week intervention focusing on leveraging individual strengths, goal setting, and resilience building. Outcomes were measured using the General Self-Efficacy Scale and the Satisfaction with Life Scale. To examine these differences, an analysis of variance with repeated measures, coupled with Bonferroni’s post-hoc test, was conducted using SPSS-26. Results indicated significant improvements in both self-efficacy and life satisfaction in the experimental group compared to the control group. The study concludes that strength-based therapy positively impacts athletes' psychological well-being, suggesting its potential for broader application in sports therapy.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".