Challenges and stresses experienced by athletes and coaches leading up to the Paralympic Games
Bibliographic record
Abstract
The demands of high-performance sport are exacerbated during the lead up to the Major Games (i.e., Paralympics). The purpose of this study was to better understand the challenges experienced and strategies utilized by Australian athletes (n = 7) and coaches (n = 5) preparing for the Tokyo Paralympic Games using semi-structured interviews. The thematic analysis highlighted challenges specific to participants' sport (e.g., budgetary constraints, decentralized experiences, athletes with various impairments), personal life (e.g., moving cities to access coaching, postponing vocational/educational developments, isolation from social circles), and associated uncertainties (e.g., COVID-19, qualifications, accreditations). Participants managed these challenges by utilizing strategies to 'anticipate and prepare' (e.g., detailed planning, effective communication, contingency plans) and 'manage expectations' (e.g., understanding specific roles and boundaries, focusing on the process [i.e., effort over results]). Trust and communication between athletes and coaches was key in coaches' better understanding of how athletes' impairments interact with their training and competition environments and tailor support to each athlete's unique needs. Last, participants reflected on the 'pressure' of the Games due to their performance having an impact on their career trajectory 'post-Tokyo' with some athletes contemplating retirement and others realizing the consequences of their performance on sport-related vocation and sponsorship. Coaches also accepted the success of their programs and job security will depend on outcomes at the Games. The findings from this study shed light on factors to consider to reduce challenges for teams preparing for major competitions but also highlight key practical implications to support athletes and coaches leading up, during, and post-major Games.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".