Optimal well-being after depression and anxiety in Canada
Bibliographic record
Abstract
We will use data from the Canadian Community Health Survey - Mental Health (CCHS-MH) to explore optimal well-being (OWB) after depression and anxiety in a nationally representative Canadian sample. We will also examine if our OWB criteria selects a different rate of individuals than Keyes (2005) complete mental health criteria. Additionally, we will examine clinical and psychosocial correlates of OWB. Research Questions (1) What is the prevalence of OWB after depression and anxiety in a Canadian sample? (2) How does the current study’s definition of OWB (Rottenberg et al., 2018) compare to other definitions of high functioning after psychopathology (e.g., complete mental health; Keyes, 2002; Fuller-Thompson, Agbeyaka, LaFond, & BerKlug, 2016)? (3) What are the clinical correlates and predictors of OWB after depression and anxiety? (4) What is the prevalence of OWB after additional disorders, including bipolar 1 and 2, alcohol or drug abuse, and suicide ideation or attempts?
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.694 | 0.464 |
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; both teacher heads agree on what is shown here.
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".