Moderation Effect of Physical Activity on the Relationship Between Fear of COVID-19 and General Distress: A Pilot Case Study in Arabic Countries
Bibliographic record
Abstract
Aims This study aimed to investigate the effects of the fear of COVID-19, level of physical activity, and gender on negative stress (distress) in an Arab population by means of structural equations based on partial least squares. Materials and Methods The sample population comprised of 459 participants from four Arab countries (age M = 33.02, SD = 8.46; n = 237 women and n = 222 men). The level of education was basic (<9 years of study; n = 144), secondary/vocational (between 9 and 12; n = 178), and university (n = 137). The “Fear of COVID-19” Scale, the short form of the “International Physical Activity Questionnaire,” and the “Perceived Stress Scale” questionnaires were disseminated by emails and social networks via Google Forms. SMARTPLS software version 3.2.9 was used to model the relationships between the variables under study. Results Results confirmed the links between level of physical activity, fear of COVID-19, and gender, showing a significant mediating effect of the fear of COVID-19 on the relationship between gender and general distress. The level of physical activity was also found to influence the fear of COVID-19, varying depending on gender. In addition, the model highlighted the presence of a moderation effect of the level of physical activity. Conclusion Based on the model presented in the present study, we can conclude that the COVID-19 pandemic has a profound impact on psychological distress in the target populations. The impact of the level of physical activity on psychological distress is shown to be very important during the pandemic phase.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".