Examining the dual continua model of mental health in student-athletes
Bibliographic record
Abstract
The Dual Continua Model (DCM) views mental health and mental illness as two separate but related constructs. The current study was designed to assess the factor structure and concurrent validity of this DCM with an intercollegiate sport sample. The 2022 Canadian cohort (N = 345; 65% female) of the National College Health Assessment completed the Mental Health Continuum-Short Form, the Kessler K6, the Connor Davidson Resilience Scale, and the UCLA Loneliness Scale. A Confirmatory Factor Analysis of obliquely related factors of mental health and illness showed strong fit of the model to the data (CFI = 0.997; RMSEA = 0.027). ANOVAs comparing different sub-groups within the DCM showed that resilience and loneliness differed among groups according to their levels of mental health and/or illness in manners consistent with the model. • The Dual Continuum Model of mental health proposes that mental health and mental illness are separate but related constructs. • This model has broad support in a variety of contexts. • The current study examined the fit of the Dual Continua Model in intercollegiate sport. • CFA and ANOVAs on the outcomes of loneliness and resilience with a sample of student-athletes were consistent with the model. • These results have significant implications for mental policy in intercollegiate 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| 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".