An uneven playing field: A mixed methods, multiphase feasibility study of a programme to reduce gambling among at-risk men in a professional football club setting
Bibliographic record
Abstract
BackgroundSports betting is a growth area for the gambling industry, with football fans a key target ofadvertising. Men are particularly at risk from gambling harm. Prior research has shown thatthe delivery of health behaviour-related interventions through professional football clubscan attract and support health behaviour change among men. The 8-week Football Fansand Betting (FFAB) programme was designed as an early health behaviour changeintervention for delivery in professional football clubs by club community coaches toreduce their sports betting and other forms of gambling among men aged 18-55 with aPGSI score of 15 and under. This paper reports the acceptability and feasibility of delivering thisprogramme in football clubs in England.MethodsThe FFAB feasibility study’s objectives were to determine whether FFAB has the potentialto reduce gambling behaviour by assessing: 1) recruitment to and retention in theprogramme, 2) the acceptability of FFAB to both club coaches and participants, and 3) togather preliminary evidence on potential impact. To meet aim 1, we initiated recruitmentat 6 clubs and recorded recruitment data throughout, and conducted focus groups onrecruitment materials with fans of Club C-F. For aim 2, we collected data on retentionthroughout our programme deliveries. For aim 3, we completed delivery of FFAB at asmany clubs as possible (n=3); observed the delivery of sessions; completed semi-structuredinterviews with coaches, participants, and non-completers.Results1) Recruitment to the programmeRecruiting the intended number of participants proved highly challenging. This was partly dueto the commercial landscape, where sponsorship by gambling companies lessened club interestin the programme. Even in clubs without direct gambling sponsors, FFAB ads struggled forvisibility against more prominent gambling sponsorships in stadiums. Moreover, stigmasurrounding gambling harm hindered participant recruitment and retention, compounded bybroader commercial challenges. Engaging the target group of the FFAB model was difficultbecause they perceived gambling as normal, making it challenging to connect with FFAB'sobjectives.2) Retention in the programmeRetention to the programme faced similar challenges as recruitment – the commercial landscapeand stigma being key challenges FFAB did not overcome. Retention at our first two programmedeliveries struggled. The final delivery used a more local-hub approach to recruitment andbolted FFAB’s classroom element on to an existing social football game and was successful inretaining its small cohort from session 0-9.3) The acceptability of FFAB to both club coaches and participantsCoaches and participants reported that FFAB was acceptable and that there is a need forearly intervention delivered in this setting. Participants who went through the programmeand the coaches who delivered it reported positive experiences of FFAB.4) Gather preliminary data on potential impactAlthough we were able to carry out our research the FFAB programme facedchallenges. This meant that it was not possible to progress to an RCT to assess impactas intended. However preliminary data from men who completed the programmesuggest that 1) there is a need for an intervention to help men address gambling harm inthe sports space, 2) these men are an underserved population, with sometimes complexneeds, and 3) they often do not recognise the signs of gambling harm and experiencestigma around their behaviour.ConclusionsWe found that FFAB was acceptable to the coaches who delivered it and the participantswho attended. However, our model for recruitment did not work. We also faced difficultieswith retention. More feasibility work to develop a different approach to a gamblingreduction with men between 18-55 with a PGSI score of less than 15 is required.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".