Masters Athletes’ Views on Sport Psychology for Performance Enhancement and Sport Lifestyle Adherence
Bibliographic record
Abstract
This study explored the views of Canadian Masters athletes (MAs; Mage = 51, range 38–62; three men and five women) from 12 sports (10 individual and two team sports) on sport psychology for performance, experiential, and lifestyle enhancement. Using Braun and Clarke’s procedures for thematic analysis, the authors interpreted data from semistructured interviews deductively in relation to five strategic themes in which psychological skills are applied for performance enhancement. Deductive results demonstrated MAs used goal setting, imagery, arousal regulation, concentration, and self-confidence to enhance performance and obtain competitive advantages. The authors also analyzed data inductively to reveal themes related to experiential and lifestyle factors. Inductive results showed that MAs “placed priorities on sport,” which involved cognitively justifying the priority and framing sport as an outlet and as the embodiment of the authentic self. Social strategies associated with continued sport pursuit included cultivation of supportive social environments, social contracts/negotiations, social signaling, and social accountability. Strategies “to fit sport in” included integrating/twinning, scheduling, and managing commitment. Managing age-related concerns involved mindfulness and compensation strategies. Results show how MAs uniquely apply sport psychology to enhance their performance and to support sport adherence.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".