Endurance athletes' coping efforts throughout competitive suffering episodes
Bibliographic record
Abstract
Endurance athletes must effectively cope with competitive suffering, a negative affective state brought about by perceived goal failure, for optimal performance during competition. The current study aimed to distinguish the sequential coping efforts of athletes who endured shorter, and longer, episodes of competitive suffering. Eleven male and 15 female competitive runners (Mage= 35.8, SD= 12.1) completed a 5km time-trial task and were unknowingly provided with pace-times that were slowed by five percent to induce competitive suffering. Following the time-trial, suffering duration and coping function use (problem-focused, PFC; emotion-focused, EFC; and avoidance, AvC) were assessed using video-mediated recall. Prior to analysis, the sample was divided by competitive suffering duration into long and short duration groups. A significant mixed RM ANOVA interaction effect, F(4, 96) = 2.569, p < .05, partial ?2 = .097,indicated differences in coping function use between each group across three phases of competitive suffering. Post-hoc analysis revealed that the short duration group used more EFC during initiation and at the peak of suffering, while also using less AvC during the initiation phase, in comparison to long duration competitive sufferers. These findings suggest that athletes adapt their coping efforts throughout emotional episodes, and that coping function use may distinguish athletes who are quickly able to regain a positive affective state.Acknowledgments: This research was supported through funding from the Sport Sciences Association of Alberta (SSAA)
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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 | 0.000 | 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) | 0.001 | 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".