‘My Sport Won’t Pay the Bills Forever’: High-Performance Athletes’ Need for Financial Literacy and Self-Management
Bibliographic record
Abstract
This paper investigates high-performance athletes’ development of their financial literacy and self-management skills and the related organisational support available to them during their athletic careers. The data were collected from 20 retired high-performance athletes (10 male and 10 female) representing six different countries (Japan, Mexico, Portugal, Singapore, South Korea, and the UK). Thematic analysis was applied to the processing of the data and five themes emerged: (1) Funding battles: financial challenges and misjudgements; (2) Coping Strategies; (3) Support from sponsors, parents, and sport organisations; (4) Development of Financial Literacy; and (5) Life After Sport. The data indicates that athletes experienced financial challenges due to a lack of organisational support, reduced or terminated funding, and limited opportunities to access sponsorship. Typically, athletes developed their financial literacy and self-management skills by ‘self-help’ or ‘trial and error’. The findings contribute to both literature and practice by providing empirical evidence on the coping strategies adopted by athletes in order to overcome financial challenges and on the methods used in order to develop their financial literacy and self-management skills. These findings inform sport organisations and governing bodies to develop support schemes for high-performance athletes as well as deepen our knowledge of athletes’ career development and transitions focusing on the financial aspect.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".