Cracking the code for maintaining quality training in Olympic distance triathlon: lessons learnt from a squad of elite Scandinavian athletes
Bibliographic record
Abstract
OBJECTIVE: High-quality training is a key determinant of performance in the Olympic distance triathlon and is potentially influenced by a unique array of context-specific biopsychosocial factors. Our objective was to explore and describe these factors among squad members of a university-based, elite Olympic distance triathlete developmental programme. METHOD: A qualitative investigation using a visual communication tool-assisted focus group and longitudinal semistructured individual interviews was conducted. Responses were solicited from the University of Southern Denmark's elite triathlon team (n=8), and inductive coding from the focus group formed the basis of questions for the two rounds of individual interviews 11 months apart. All interviews were transcribed verbatim and then analysed thematically. RESULTS: Seventeen context-relevant factors were identified and 10 themes emerged, these being 'the cold weather ritual', 'digestive system conditioning', 'the curse of the night owl', 'the strings attached to sponsorship', 'my coach-my rock', 'mood maintenance', 'the asynchronous training rhythm', 'psychological slavery', 'the legacy of the asphalt tattoo' and 'the tension of family and friends'. CONCLUSIONS: By reflecting on their personal training vortex, elite triathletes were able to provide context-relevant insights into the maintenance of training quality over the course of a competitive season. Further research is required to elucidate whether and how biopsycholosocial factors can be modified to optimise the achievement of training goals.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".