A scoping review of factors within the higher education ecosystem influencing student-athlete mental health and wellbeing in North America: Insights and a model for mental health promotion
Bibliographic record
Abstract
The purpose of this research was to synthesize peer-reviewed literature identifying factors in the organizational ecosystem that impact post-secondary student-athlete mental health in North America. We adopted a holistic definition of mental health considering outcomes related to both mental illness and multidimensional wellbeing (i.e., psychological, emotional, and social wellbeing). A structured scoping review method was used to search seven databases. Data from included studies (N = 57), were summarized according to the socioecological model of health and analyzed using thematic synthesis. Post-secondary sport environments that promoted mental health and wellbeing supported student-athlete psychological need satisfaction and were characterized by: (1) growth-oriented motivational climates, (2) harmony between academic and athletic roles, (3) equity and inclusion, (4) social support, (5) positive relationships, (6) ethical leadership, and (7) health-promoting organizational operations. We propose a theoretically and empirically informed conceptual model illustrating features of post-secondary sport that interact to promote student-athlete mental health and wellbeing. These findings illustrate the need for multidimensional approaches to mental health promotion and further organizational research, particularly in Canada. This review highlights the mental health impacts of environmental demands and resources that can support effective institutional interventions to safeguard student-athlete mental health despite inherent stressors of competitive sport.
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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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.023 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".