The time courses of runners’ recovery‐stress responses after a mountain ultra‐marathon: Do appraisals matter?
Bibliographic record
Abstract
Abstract Objectives : The aim of this study was to: (a) examine the time courses of runners’ recovery‐stress states within the month following a demanding Mountain Ultra‐Marathon (MUM) race; and (b) explore the role of primary and secondary appraisals in these trajectories. Design : A seven‐wave one‐month longitudinal design was used with one measurement point within two days before the race to measure appraisals and six time points within the month following the race to assess recovery‐stress states experienced by athletes. Method : A multilevel growth curve analysis approach was used among a sample of 29 MUM runners. Results : Recovery‐stress states were characterized by distinct trajectories during the month following MUM race. Results of multilevel growth curve analyses showed significant linear increases of general and total recovery, significant linear decreases of general, sport‐specific and total stress and a positive quadratic effect of squared time (U shape over time) on specific recovery. Primary appraisal significantly positively predicted levels of sport‐specific recovery, total, general and sport‐specific stress and significantly negatively predicted total and general recovery. Secondary appraisal significantly negatively predicted total and general stress. Conclusions : This study provided insights into the role played by appraisals on the recovery‐stress states experienced by MUM runners the month following a demanding MUM race. Operational strategies were suggested in order to optimize the recovery‐stress balance and in turn psychological adaptation processes in response to an ultra‐endurance race.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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; both teacher heads agree on what is shown here.
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".