Gender differences in general and specialty outpatient mental health service use for depression
Bibliographic record
Abstract
BACKGROUND: This study ascertained gender-specific determinants of outpatient mental health (MH) service use for depression to highlight any gender disparities in barriers to care and explain how depressed men and women in need of care might differ in their help-seeking behaviour. METHODS: Data used in this study came from the Canadian Community Health Survey on Mental Health and Well Being, cycle 1.2 (CCHS 1.2) conducted by Statistics Canada in 2002 (N = 36,984). The sample was limited to respondents filling criteria for a probable major depression in the 12 months prior to the interview (n = 1743). Gender-specific multivariate logistic regression analyses were carried out. RESULTS: The results showed that 54.3% of respondents meeting criteria for major depression had consulted for mental health reasons in the year prior to interview. When looking at type of outpatient mental health service use, males were more likely to consult a general practitioner and a mental health specialist in the past year as opposed to females. However, females were more likely to consult a general practitioner only as opposed to no service use than males. Gender specific differences in determinants associated with outpatient service use included for males, lower adjusted household income, and for females, a younger age, the presence of social support, self-reported availability barriers, the presence of self-reported suicidal thoughts or attempt and a poorer self- perceived mental health. CONCLUSIONS: Continued efforts to promote access to mental health care are needed for men and women affected by depression, and this, to target specific vulnerable populations and increase utilization rates.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".