Tackling mental health: the role of professional football clubs
Bibliographic record
Abstract
In the UK, professional football clubs are being used as settings for the delivery of interventions that promote mental health in a number of ways including (i) the delivery of physical activity interventions to improve the mental health of the general population, (ii) the delivery of physical activity interventions for people experiencing mental illness, and (iii) the delivery of community mental health services within the confines of the football club. This research note offers insights into mental health interventions delivered within, and by, professional football clubs and the available evidence concerning their reach, effectiveness and impact. The findings suggest that professional football clubs can help to facilitate access to mental health services, particularly among young people, for whom accessing such services may be highly stigmatized. Furthermore, the findings highlight that such interventions have a positive impact on health. However, in order to capitalize on this opportunity funding agencies and commissioners must provide appropriate resources (human and financial) for effective delivery and evaluation. Furthermore, a more strategic approach to working towards the mental health agenda must be adopted. It is argued that this change in practice would allow professional football clubs to offer those in need access to high-quality interventions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".