Understanding student-athlete mental health problems: knowledge, experiences, and management
Bibliographic record
Abstract
Compounding athletic and academic demands may put student-athletes at an increased risk of developing mental health problems. Few studies have examined the mental health experiences of student-athletes in Canada using both quantitative and qualitative methods. In two studies, this dissertation (1) assessed the scope and severity of mental health problems and factors associated with mental health problems (study 1), (2) qualitatively explored the impact of the sport context on student-athletes’ mental health experiences, knowledge, and management strategies (study 2), and (3) examined the impact of COVID-19 on student-athletes’ mental health (studies 1 and 2). In Study 1, student-athletes completed an online survey at three timepoints in their athletic season (September 2019 (T1; N = 140), February 2020 (T2; N = 18) and May 2020 (T3; N = 28)). Data from T1 and T3 were utilized due to significant missing data at T2. Participants were members of a central Canadian university sports program for the 2019-2020 season. At T1 (N = 140), nearly half of the sample exceeded the clinical cut-off on one or more measures. Approximately half of the sample reported significant depressive, anxiety, and distress symptoms and nearly 40% exceeded the clinical cut-off for probable PTSD. Mental health literacy (M = 2.76, SD = .90) was low to moderate, and positively related to anxiety and having received a past mental health diagnosis. Only disordered eating symptoms were significantly different between T1 and T3 (p = .05). Qualitative findings helped to contextualize these results. In Study 2, student-athletes (N = 7) with clinically significant distress (> 12 on the Kessler 6-Item Distress Scale) completed individual virtual semi-structured in-depth interviews. Using an Interpretive Description analytic approach, the overarching theme, “The Athlete Identity: The Tensions in Upholding Athlete Standards” underscored participants responses and described the implicit expectations that athletes were expected to uphold. In addition to this overarching theme were four main themes: “Shaping Identity”; “Navigating the Complexities of Disruptions to the Athlete Identity”; “The Struggles with Struggling: Making Sense of Mental Health”; and “Reconciling Mental Health and The Athlete Identity”, that described the challenges with managing mental health in a competitive sport context. Findings from this dissertation inform clinical interventions to address mental health in sport. Implications regarding the assessment and management of mental health symptoms in sport are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".