Tiredness, Fatigue, and Exhaustion as Perceived by Recreational Marathon Runners
Bibliographic record
Abstract
In this article, we report the results of a study that was part of a five-study concept development project. Our goal was to learn about the nature of illness by exploring variations in the manifestations of fatigue, a symptom that is prevalent in both ill (cancer, depression, chronic fatigue syndrome) and selected nonill (recreational marathon runners, shift workers) populations. In this article, we report results of our study of recreational marathon runners, obtained from unstructured interviews with 13 runners between the ages 19 and 49 years using ethnoscience as the design. Key findings with implications for practice are the importance of planning recovery periods following large energy expenditures, the value of using dissociative strategies to manage tiredness, and the usefulness of associative strategies and support systems to manage fatigue. Future studies could explore whether these strategies would be useful for management of tiredness and fatigue in other populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".