A-46 Deconstructing Competitiveness: The Effect of Age on Athlete’s Desire to Succeed, Win, and Achieve Goals
Bibliographic record
Abstract
Abstract Objective Our previous research indicated that athletes scoring high in competitiveness were less likely to report sports-related concussion symptoms and withdraw from the game. The present study examined whether athletes’ desire to succeed (competitiveness motive), win (win orientation), and achieve personal goals (goal orientation) were related to the age of players. Method Participants included 161 athletes, ages 14–32 (M = 17.6 years; 33.2% female), recruited from a mid-sized Canadian city participating in low (rowing), moderate (soccer) and high (hockey, rugby, football) contact sports. Confirmatory factor analysis was first used to evaluate the structure of the Sports Orientation Questionnaire. Then, using SEM, athletes’ competitiveness, goal orientation, and win orientation were predicted by age. Results High internal consistency was obtained within each factor (.84–.93). The model demonstrated suboptimal fit for this sample (CFI = .84; χ2f/df ratio = 2.02; RMSEA = .087; 90% CI: .077–.097). All factors were significantly related to age, indicating that athletes’ desire to succeed (competitiveness motive; β = .18, p = .009), achieve personal goals (goal orientation; β = .26, p = .007), and win (win orientation; β = .30, p = .000) increases with age. Conclusion The small positive association between age and competitiveness, win, and goal orientation indicates that older athletes are more competitive than younger athletes. Given that competitiveness predicts athletes’ intention to report a concussion, clinicians and coaches should pay particular attention to senior athletes who demonstrate high levels of general competitiveness and who are driven by the desire to win and achieve personal goals. Therefore, interventions targeting the barriers to reporting concussions should evaluate subco.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".