Can a Healthy Lifestyle Reduce Feelings of Anxiety during the COVID-19 Pandemic?
Bibliographic record
Abstract
The implemented outdoor activity restrictions throughout the COVID-19 pandemic have caused a change in people's lifestyles. The significant changes that can be seen are the reduced number of people's participation in physical activities and the change in their dietary patterns [1]. This condition also affects the anxiety of everyone. A lifestyle related survey was conducted on 274 students at the University of Education Indonesia using the Fantastic Instrument developed by Dr. Douglas Wilson from the Department of Family Medicine, McMaster University Canada. The lifestyle itself here includes conditions from sports activities, rest periods, and feelings of anxiety during the COVID-19 pandemic. The results of the analysis show that there is a significant influence of life on feelings of anxiety with a sig (2-tailed) value of 0.046 < 0.005, while the coefficient of determination (R Square) of 0.11 means that lifestyle variables have an effect of 11% on anxiety, the rest is influenced by other factors. The implementation of activities during the Covid-19 period had a real impact on the lifestyle of students at the Indonesian Education University, significantly reducing feelings of anxiety for those who applied a good lifestyle and vice versa, feelings would increase when the lifestyle was not good.
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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".