COVID-19 Pandemic and Physical Exercise: Lessons Learnt for Confined Communities
Bibliographic record
Abstract
The novel pandemic called "Coronavirus Disease 2019" (COVID-19), as a global public health emergency and global threat, has affected many countries in unpredictable ways and impacted on physical activity (PA) behaviors to various extents. Specific populations including refugees, asylum seekers, and prisoners, are vulnerable groups with multiple complex health needs and worse health outcomes with respect to the general population worldwide and at high risk of death from the "Severe Acute Respiratory Syndrome-related Coronavirus type 2" (SARS-CoV-2). Governments around the world have been implementing preventive healthcare policies, including physical and social distancing, isolation, and confinement, to mitigate against the burden imposed by the COVID-19 outbreak. This pandemic period is characterized by reduced or lack of movement. During this period of lockdown, PA can represent an immunotherapy and a preventative approach to avoid the harmful effects of inactivity due to the pandemic. Moreover, PA could be prescribed to improve the immune system of specific populations (refugees, asylum seekers, and prisoners), which particularly experience the condition of being confined. The present narrative review discusses the potential impacts of COVID-19 pandemic on these specific populations' health status and the importance of performing PA/exercise to reduce the deleterious effects of COVID-19 pandemic. In addition, we aim to provide useful recommendations on PA/exercise for these specific populations to maintain their level of independence, physical, and mental health as well as their wellbeing.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".