“We missed the psychological support”: A case study about the preparation of the Brazilian bronze medal kata team for the 2019 Pan American Games
Bibliographic record
Abstract
Purpose: The main aim of the study was to describe the key factors involved in the preparation process of the Brazilian bronze medal kata team for the 2019 Pan American Games, focusing on the athletes' perceptions. Methods: Three male athletes from the Brazilian team performed a semistructured interview to identify the following topics: specific time for preparation, training organization, supplementary support, and perception and suggestion about the efficiency of the preparation process. Results: Data from interviews were gathered and coded, and the major themes were summarized as follows after performing content analysis of the data: (a) technical and tactical training took the major part of the preparation; (b) the high level of the coaches helped the team to reach the technical quality of the kata; (c) better psychological support during the preparation could improve the athletes' performance during the training and competition; and (d) the lack of financial support compromised the commitment of the athletes during the training routine. Conclusion: We concluded, based on the athletes' perception, that the most positive factor during preparation for a major competition was the high amount of time focused on technical-tactical training. Even with limitations to performing the physical training, the athletes recognized the importance of the physical component, to increase performance. Financial and psychological support could have helped the team to reach a better result (gold medal) attenuating the training distress.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".