FIFA's<i>Football for Health</i>: applying Kotter's eight-step programme for transformational change to a mass participation activity
Bibliographic record
Abstract
Warm upFIFA President Sepp Blatter deserves tremendous credit.Blatter's leadership has been remarkable for moving health from irrelevance to preeminence in a major sport. 2 Blatter and the Chair of the FIFA Medical Assessment and Research Centre (F-MARC), Professor Jiri Dvorak, supported by Dr Michel D'Hooghe, Chair of FIFA Medical Committee, convened national sporting organisation leaders together with a sports medicine lead from almost 200 countries to the fi rst Football for Health conference (Zurich, 2009).That is how FIFA/F-MARC conveyed a sense of urgency.The meeting was not a talk-fest, but carefully structured to obtain buy-in for the concept of having active 'medical commissions' in each country.One goal for FIFA. A POWERFUL GUIDING COALITION WITH EXPERTISE AND RELATIONSHIPSKotter tells us that 'nothing much worthwhile happens' without a critical mass willing to encourage others to engage in change. 1 It takes a coalition to create transformational change such as the concept of Football for Health (which is outlined below).FIFA's health efforts stem back to the FIFA Board acting cohesively (ie, as a team) to support F-MARC, 2 which brings together international groups of experts in football medicine to support both professional and recreational football players.F-MARC is critical to the Football for Health programme because it is the core of the guiding coalition that supports the health of players.This has many advantages over a 'medical director' acting alone.Furthermore, F-MARC is represented on the executive committee of FIFA by Dr Michel D'Hooghe, who skilfully explains and promotes medical matters at this critical political platform.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".