Mental Health Literacy and Ontario Young People: Major Depressive Disorder, Bipolar Disorder, and Generalized Anxiety Disorder
Bibliographic record
Abstract
This study examined the mental health literacy of a group of young people, with particular \ninterest to their ability to correctly label, identify symptoms, and recommend appropriate help \nsources for Major Depressive Disorder, Generalized Anxiety Disorder, and Bipolar Disorder. \nRespondents were 88 Ontario, Canada residents (26 males, 62 females) aged 18-24. Respondents \nprovided mixed knowledge in ability to determine whether an individual was dealing with \nmental illness as well as mixed knowledge in labeling the mental disorders examined and \nidentifying the symptoms of each disorder. Respondents were significantly more likely to \ncorrectly label Major Depressive Disorder opposed to Generalized Anxiety Disorder and Bipolar \nDisorder. As well, respondents were significantly more likely to label appropriate symptoms for \nMajor Depressive Disorder opposed to Generalized Anxiety Disorder and Bipolar Disorder. Our \nfindings suggest that young people have a greater mental health literacy for Major Depressive \nDisorder opposed to Generalized Anxiety Disorder and Bipolar Disorder. Results are discussed \nin light of prior adolescent and young adult mental health literacy and clinical implications.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".