Time courses of emotions experienced after a mountain ultra-marathon: Does emotional intelligence matter?
Bibliographic record
Abstract
This research examined the time courses of emotions in sport settings (anxiety, dejection, anger, happiness, excitement) experienced by mountain ultra-marathon (MUM) runners within the month following a demanding MUM race and the role of emotional intelligence (EI) in these time courses. A six-wave one-month longitudinal design was used with one measurement point within two days before the race to measure EI and five time points within the month following the race to assess emotions experienced among a sample of 29 runners. Results of multilevel growth curve analyses showed significant linear decreases of dejection and anxiety and a significant linear increase of anger. EI was related to the intercept (level at the end of the MUM race) of happiness, excitement and dejection. Moreover the interaction of EI with time was associated with happiness, excitement and anger. This means that high and low emotional intelligent runners exhibited distinct trajectories of emotional intelligence within the month following the MUM race. Indeed, trait-EI appeared to have a protective role against stress process leading to emotional adjustment within the recovery period following an ultra-endurance event. As such, consultants and coaches could conduct specific program over the sport season designed to enhance trait-EI of MUM runners.
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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 | 0.002 |
| 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 | 0.001 | 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".