Philanthropy and humor: Unlocking the psychological tool box of an ultra-endurance athlete
Bibliographic record
Abstract
Despite growing physical inactivity at the population level, there is rising interest in extreme physical challenges such as ultra-endurance events. While much research has focused on psychological characteristics of elite athletes (e.g., Durand-Bush & Salmela, 2002), less work has examined these characteristics among 'weekend warriors'. The purpose of this case study was to explore the psychological journey of a 44 year old male athlete, who engaged in a solo running pursuit of the mountainous 276 kilometer Cabot Trail in Nova Scotia, Canada. Data were collected through semi-structured interviews before, during and following the event, focusing on the participant's personal background, preparation, challenges, expectations, and reflections; media and social networking sources were also examined. Themes emerged in three broad categories. The participant's initial related to his desire to challenge himself, challenge others' mindsets and make a contribution. In the moment motivation was drawn from social support and feelings of making a difference, while strategies for persistence involved focusing on within event goals, using positive self-talk, and creating a humorous environment. Finally, ongoing was maintained through a persistent desire to make a difference, motivate others, and push past 'failure'. Findings are discussed in the context of previous work, future directions and practical implications.Acknowledgments: York University Faculty of Health
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".