Depression Literacy in Alberta: Findings from a General Population Sample
Bibliographic record
Abstract
OBJECTIVE: To assess the public's knowledge about depression, attitudes toward treatments for depression, perceived causal factors for depression, and reported prognoses of depression, overall and by sex. METHODS: We conducted a cross-sectional telephone survey in Alberta between February and June 2006. We used a random phone number selection procedure to identify a sample of adults in the community (n = 3047). Participants were presented with a vignette describing an individual with depression and then asked questions to assess recognition of depression, attitudes toward mental health treatments, possible causal factors for depression, and prognosis of depression. RESULTS: The response rate was 75.2 %. Among the final participants, 75.6% could correctly recognize depression described in a case vignette. General practitioners or family doctors were considered as being the best help for depression. Of the participants, 35% were in complete agreement with health professionals about appropriate interventions for depression, 28% believed in dealing with depression alone, and 43% thought that "weakness of character" was a likely cause of depression. Men had poorer mental health literacy than women and were more likely to endorse the use of alcohol to cope. CONCLUSIONS: Mental health promotion and education efforts are needed to improve the general public's mental health literacy and to clarify misunderstanding about depression. Men need to be a particular target of these efforts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".