Negative Attitudes toward Help Seeking for Mental Illness in 2 Population—Based Surveys from the United States and Canada
Bibliographic record
Abstract
OBJECTIVES: To determine the prevalence and sociodemographic correlates of negative attitudes toward help seeking for mental illness among the general population in the United States and Ontario. METHODS: Two contemporaneous population-based surveys (aged 15 to 54 years) were analyzed: the US National Comorbidity Survey (NCS) (n = 5877) and the Ontario Health Survey (OHS) (n = 6902). Multiple logistic regression analyses were used to examine the correlates of a derived negative attitudes composite variable obtained from questions assessing probability, comfort, and embarrassment related to help seeking for mental illness. RESULTS: Negative attitudes toward help seeking for mental illness were prevalent in both countries. Fifteen percent of OHS and 20% of NCS respondents stated they probably or definitely would not seek treatment if they had serious emotional problems. Almost one-half of recipients in both surveys stated they would be embarrassed if their friends knew about their use of mental health services. Negative attitudes toward help seeking were highest among socioeconomically challenged young, single, lesser-educated men in Ontario and the United States. In both countries, substance abuse or dependence and antisocial personality disorder were associated with greater negative attitudes, as was not having sought treatment in the past. CONCLUSIONS: Negative attitudes toward mental health service use are prevalent in Ontario and the United States. They are most common in young adults, especially those with lower education and socioeconomic resources, and those with substance abuse or dependence problems. This information can be used to target educational efforts aimed at improving willingness to seek care for mental health problems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".