Momentum During a Running Competition: A Sequential Explanatory Mixed-Methods Study
Bibliographic record
Abstract
International Journal of Exercise Science 13(5): 615-632, 2020. The purpose of this study was to better understand the psychological momentum (PM) in varsity cross-country competitive runners during a 3000 m selection trials. A sequential explanatory mixed methods design was used: recruitment trial race day (quantitative) and interview day (qualitative + maximal aerobic running speed [MARS]). Sample was consisted of fifteen university distance runners (n = six women [25.9 ± 7.0 years old; 22.2 ± 1.8 BMI] and nine men [23.2 ± 2.4 years old; 22.6 ± 1.6 BMI]). During the recruitment trial race, athletes’ MARS was measured and used to create a performance index (PERFI) relative to selected moments. Also, the recruitment trial race was filmed. During the interviews, the recorded film was used to support athletes in the identification of key moments of the race, as well as to discuss positive and negative PM. PM was both defined by participants and devised by three themes: psychological, physiological and psychophysiological change. A significant PERFI difference (p < 0.001) was observed between positive (97.04 ± 5.88%) and negative (108.46 ± 7.76%) moments of PM. The results of PERFI for men and women athletes were not significantly different (p = 0.118). The PERFI standard deviation for women was not correlated (r2 = 0.26, p = 0.30) with the 3000 m time trial performance, but it was significantly correlated for men (r2 = 0.94, p < 0.001). The results of the present study could help developing interventions to focus on specific elements of the momentum such as race management/strategy, the attentiveness of the runner during the race and other elements of mental and physical preparation of the athletes.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".