Internal consistency reliability of mental health questionnaires in college student athletes
Bibliographic record
Abstract
OBJECTIVES: To examine the internal consistency reliability and measurement invariance of a questionnaire battery designed to identify college student athletes at risk for mental health symptoms and disorders. METHODS: College student athletes (N=993) completed questionnaires assessing 13 mental health domains: strain, anxiety, depression, suicide and self-harm ideation, sleep, alcohol use, drug use, eating disorders, attention deficit hyperactivity disorder (ADHD), bipolar disorder, post-traumatic stress disorder (PTSD), gambling and psychosis. Internal consistency reliability of each measure was assessed and compared between sexes as well as to previous results in elite athletes. Discriminative ability analyses were used to examine how well the cut-off score on the strain measure (Athlete Psychological Strain Questionnaire) predicted cut-offs on other screening questionnaires. RESULTS: Strain, anxiety, depression, suicide and self-harm ideation, ADHD, PTSD and bipolar questionnaires all had acceptable or better internal consistency reliability. Sleep, gambling and psychosis questionnaires had questionable internal consistency reliability, although approaching acceptable for certain sex by measure values. The athlete disordered eating measure (Brief Eating Disorder in Athletes Questionnaire) had poor internal consistency reliability in males and questionable internal consistency reliability in females. CONCLUSIONS: The recommended mental health questionnaires were generally reliable for use with college student athletes. To truly determine the validity of the cut-off scores on these self-report questionnaires, future studies need to compare the questionnaires to a structured clinical interview to determine the discriminative abilities.
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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 | 0.023 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".