Scale-Up and Scale-Out of a Gender-Sensitized Weight Management and Healthy Living Program Delivered to Overweight Men via Professional Sports Clubs: The Wider Implementation of Football Fans in Training (FFIT)
Bibliographic record
Abstract
Increasing prevalence of obesity poses challenges for public health. Men have been under-served by weight management programs, highlighting a need for gender-sensitized programs that can be embedded into routine practice or adapted for new settings/populations, to accelerate the process of implementing programs that are successful and cost-effective under research conditions. To address gaps in examples of how to bridge the research to practice gap, we describe the scale-up and scale-out of Football Fans in Training (FFIT), a weight management and healthy living program in relation to two implementation frameworks. The paper presents: the development, evaluation and scale-up of FFIT, mapped onto the PRACTIS guide; outcomes in scale-up deliveries; and the scale-out of FFIT through programs delivered in other contexts (other countries, professional sports, target groups, public health focus). FFIT has been scaled-up through a single-license franchise model in over 40 UK professional football clubs to 2019 (and 30 more from 2020) and scaled-out into football and other sporting contexts in Australia, Canada, New Zealand, England and other European countries. The successful scale-up and scale-out of FFIT demonstrates that, with attention to cultural constructions of masculinity, public health interventions can appeal to men and support them in sustainable lifestyle change.
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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 | 0.020 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".