The COVID-19 pandemic: how to maintain a healthy immunesystem during the lockdown – a multidisciplinary approach withspecial focus on athletes
Bibliographic record
Abstract
On January 31, 2020, the World Health Organization (WHO) declared the outbreak of a novel coronavirus responsible for an infection termed COVID-19 as a global public health emergency. To slow the spread of the coronavirus, countries around the world have been implementing various measures, including school and institutional closures, lockdown and targeted quarantine for suspected infected individuals. More than a third of the world's population have been home confined less than 4 months after the start of the outbreak. The present article aims to advise healthy individuals and athletes who are in lockdown regarding their lifestyle in order to keep healthy, safe and fit. The advice contained in the present article could apply to anyone aiming at remaining in good physical and mental health while forced to undergo lockdown, quarantine, or limited movement (movement control order). Boosting the immune system is crucial during such periods for confined people and especially for confined athletes. Specific recommendations must be followed concerning boosting the immune system through physiological and psychological management. This article analyses the available scientific evidence in order to recommend a practical approach, focusing on nutrition, intermittent fasting or caloric restriction, vitamin D insufficiency, sleep pattern, exercise, and psychodynamic aspects as factors impacting the immune system and human health in general.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".