The Dual Continuum Model of Mental Health: Investigating the Difficulties of Canadian University Student-Athletes
Bibliographic record
Abstract
The Dual Continuum Model of Mental Health (DCM) classifies individuals, based on their mental health functioning and mental illness status, as completely mentally healthy (CMH), moderately mentally healthy (MMH), purely languishing (PL), purely mental illness (PML), and complete mental illness (CMI) (Keyes, 2005). The DCM has been shown to predict outcomes such as limitations of activities with daily living, and workdays lost or cutback in adults (Keyes, 2002). Post-secondary student-athletes are a unique population due to having the same educational demands as their non-athlete peers with the added workload associated with varsity sport (Egan, 2019). Sleep disorders and low academic achievement have been seen to be prevalent in university student-athletes (Hall et al., 2017; Ebert et al., 2018). This study investigates the relationship between the DCM classifications and difficulties with academics, sleep, intimate relationships, and other social relationships in Canadian university student-athletes. The ACHA’s NCHA 2019 Canadian Reference Group was analyzed. Chi Square tests revealed significant grouping differences across DCM classifications and difficulty with academics (p
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".