Waves of well-being: exploring surfing’s multidimensional health benefits and key influencing factors
Bibliographic record
Abstract
Over the past two decades, research on outdoor activities such as surfing has grown significantly, highlighting their benefits across dimensions of health. While numerous studies have established the physical and mental health advantages of surfing, less attention has been paid to key factors influencing these effects, despite their importance in understanding how this activity impacts well-being. Bridging this gap is essential to ensuring a more comprehensive perspective on its benefits and applications. This article presents an exploratory qualitative study based on interviews with ten surfers aged 24 to 41. Through thematic analysis, the results provide deeper insight into the effects of surfing, while identifying key factors influencing these outcomes. By capturing direct experiences of surfers, this study enhances our understanding of how surfing fosters well-being beyond its traditionally acknowledged benefits. In conclusion, the findings offer practical applications for recreational programming, therapeutic interventions, and community health initiatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".