Flourishing in Life: An Empirical Test of the Dual Continua Model of Mental Health and Mental Illness among Canadian University Students
Bibliographic record
Abstract
In the conventional paradigm, mental health and illness exist on a single continuum where the emphasis is on the presence or absence of pathological outcomes. By contrast, a new theoretical framework recognizes and promotes a dual continua model where mental health is no longer the absence of mental illness. This new paradigm argues that mental health should be regarded as a ‘syndrome of symptoms’ which include the presence of positive feelings (emotional well-being) as well as positive psychosocial functioning (psychological and social well-being). Using a sample of over 1200 students from a Canadian university, the goal of the current research is to test empirically the multiple dimensions of well-being in order to address three research questions: (1) What percentages of students are flourishing, moderately healthy and languishing? (2) What is the relationship between mental health and mental illness when conceptualized on separate continua? (3) What are the significant predictors of mental health and well-being? Results support a dual continua model; while there is overlap between mental health and illness, a sizable group of respondents are ‘mentally ill’ and ‘mentally healthy’ or ‘not mentally ill’ and ‘not mentally healthy’. Students who scored higher on positive mental health tended to be female, higher in socio-economic status, more spiritual/religious, more likely to forgive, with little or no experience of childhood trauma and lower rates of depression and anxiety. These findings have implications for the conception of positive mental health beyond the ‘absence of disease.’
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".