‘Mental heAlth and well-being in rUgby pLayers’ (MAUL) study: an online survey of diverse cohorts of rugby union players internationally
Bibliographic record
Abstract
Introduction: Mental health and well-being is a relatively under-researched area in rugby, especially outside the elite men's game. Evidence suggests that physical activity and sports benefit mental health and well-being, and rugby provides health-enhancing moderate-to-vigorous physical activity. Objective: This cross-sectional study used an online approach and engaged national rugby governing bodies to understand adult rugby players' mental health and well-being and increase the diversity of the current evidence base. Results: 500 rugby players completed an online survey. 44% of participants identified as female, and 55% as male. The UK (67%), Ireland (15%) and South Africa (12%) were the countries with the highest representation. 71% of participants were amateur players, with elite players making up 20% of the population. 87% of players participated in contact forms of the game, with 9% predominantly playing non-contact rugby. Over 50% of participants reported that rugby impacted 'extremely' positively on both their mental health and well-being. Based on the Kessler psychological distress scale (K10), 57.8% of all respondents belonged to the 'psychologically well' group. Males were more likely to belong to this group than females (p=0.01). Non-contact and amateur players had lower scores of psychological distress than contact and professional players (p=0.001 and p=0.006), respectively. Non-contact players had higher well-being (Short Warwick-Edinburgh Mental Well-being Scale) scores than contact players (p<0.001). Conclusion: This study provides new insights into the mental health and well-being of a diverse group of rugby players.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".