COVID-19 Pandemic Impacts on the Mental Health of Professional Soccer: Comparison of Anxiety Between Genders
Bibliographic record
Abstract
This study verifies associated factors with trait and state anxiety in professional soccer teams during the COVID-19 pandemic. The sample was composed of 315 athletes, coaches, and physical trainers of professional soccer teams during the COVID-19 pandemic. From this amount, 214 were classified with trait anxiety, and 315 were classified with state anxiety using the State-Trait Anxiety Inventory (STAI). This study is an epidemiological and cross-sectional study. We applied an observational method, and we performed a remote measurement. The measurement was made via online questionnaires in male and female individuals working on soccer teams (soccer professionals or athletes) who could be affected by anxiety during social isolation in the COVID-19 pandemic. Each questionnaire was composed of sociodemographic questions, self-perceived performance, and STAI. The main results indicated a significant difference between female vs. male soccer professionals in state anxiety (54.97 ± 9.43 vs. 57.65 ± 9.48 index) and trait anxiety (54.21 ± 5.74 vs. 55.76 ± 6.41 index) with higher results in men. Sociodemographic variables impacted significant differences between female and male athletes and professionals of soccer clubs, and anxiety during the pandemic COVID-19 period impacted self-perceived performance analysis. The present results highlight the importance of cognitive behavior therapy for professional soccer teams.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".