Football in Times of COVID-19: A Recapitulation of Preventive Measures and Infection Control Policies Aiming at a Safe Game Environment
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) resulted in sporting event suspensions and cancellations, affecting competition calendars worldwide during 2020 and 2021. This challenged high-performance athletes' capacity to complete physical, technical, or tactical training during restricted movement measures (lockdown). With the Football World Cup organized in the last quarter of 2022, the past period of training and match disturbances challenged footballers concerning their performance and potential higher risk of injury at official matches' resumption. There has been considerable debate about the management of resuming professional football (soccer) during the COVID-19 pandemic. Governing bodies worldwide implemented measures to ensure a safe resumption of football. These precautionary measures aimed to protect the health of players, their support staff, and officials around the pitch and ensure the enjoyment of the event by spectators in the football stadiums. We have therefore narratively reviewed scientific papers about how football has resumed on the pitch and in the stands with special focus on the COVID-19 infection control strategies allowing footballers to perform again and supporters to enjoy the game after the 2020 global stop to sport.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".