Tough but not enough: Female university athletes self-reported mental toughness
Bibliographic record
Abstract
Background: Mental health and sport related mental toughness have become prominent in the media in recent years. Successful athletes cope with stressful situations by building their mental toughness in response to challenging conditions. In university, athletes are required to maintain performance standards, school standards of excellence, and continue to develop their mental toughness. This study was motivated by the limited knowledge associated with female athletes, at a unique point in their life where they are challenged both academically, socially, and in their chosen sport.\nMethods: This study proposed the following two questions; “What are the factors associated with mental toughness for elite female athletes in Ontario?” and “What is the current standing of mental health attitudes within elite female university athletes in Ontario?” This cross-sectional study used a quantitative online survey design and is grounded in a pragmatic paradigm. Mental health attitudes were measured by Community Attitudes towards Mental Health (CAMI), and the Psychological Performance Inventory- Alternative (PPI-A), a measured mental toughness.\nResults: Using purposive and convenience sampling strategies, 60 participants were recruited who self-identified as female university students under 35 years old, participating in a varsity level sport, and currently attending an Ontario university. Multiple regression analyses found two models that explain 33.4% of the variance of mental toughness, and 33.8% of the variance of mental health attitudes. Attending school, and type of sport practiced, were strong contributors to both models as well as other factors.\nConclusion: This study brings forth implications for interdisciplinary research, practice, and policy, with an emphasis on psycho-educational, mental health, and sport-specific interventions, and potential change in sport culture for female athletes. Despite limitations, the study has the potential to contribute to the scarce literature with female elite university athletes in Ontario
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".