Impact of COVID-19 and the postponement of the Tokyo 2020 Paralympic Games on Paralympic athletes and staff: A cross-sectional study
Bibliographic record
Abstract
Abstract We aimed to investigate the impact of the postponement of the Tokyo 2020 Paralympic Games on expected participants’ careers, COVID-19 history and mental health using an e-survey. Thirty-nine participants (median age 37 years; 16 females) from five countries responded between July 20 th and September 28 th , 2021, of which 37 completed the survey, including 20 athletes and 11 coaches. All but two participants planned to attend the rescheduled Games in 2021 (95%). Ninety percent (35/39) had previously tested at least once for COVID-19, with six testing positive. While three had no symptoms, all six were moderately impacted. Scores (median; lower and upper quartiles; questionnaire) for depression (2; 0.75-4; PHQ-9) and anxiety (2; 0-5.25; GAD-7) were low. Scores for post-traumatic stress disorder (PTSD) (3.5; 1-11; IES-R) were also low, but four participants reported high scores indicative of clinical concern for PTSD. There was low emotional distress caused by postponement of the Games (2; 1-4.5), and moderately low fear of catching COVID-19 (3; 2-5.5) on 10-point (0 = none, 10 = extreme) rating scales. While overall this population appears relatively resilient, the postponement of the Games came at a cost for some athletes and coaches, specifically with regards to experiencing symptoms of PTSD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".