Psychiatric Disorders and Use of Mental Health Services by Ontario Women
Bibliographic record
Abstract
OBJECTIVES: To describe the lifetime prevalence of selected psychiatric disorders in Ontario women and to compare these estimates with use of mental health resources. METHODS: We obtained data from a survey of 3062 Ontario women, aged 25 to 74 years, who participated in the Women's Health Study. A 5-item scale assessed lifetime prevalence of 5 psychiatric disorders (anxiety, depression, posttraumatic stress disorder [PTSD], obsessive-compulsive disorder [OCD], and anorexia [AN] or bulimia [BN]). We assessed use of mental health services by comorbidity. We employed stratified random sampling to select study subjects. Prevalence estimates were weighted and 95%CIs were obtained using Taylor linearization techniques (1). RESULTS: Nearly 30% of those surveyed reported at least 1 of the disorders studied. The most common were depression (27%) and anxiety (21%). Lifetime prevalence of PTSD, OCD, and AN or BN were 10.7%, 6.1%, and 3.9%, respectively. Successively younger birth cohorts displayed an increase in prevalence and a decrease in onset-age for all disorders. "Ever" use of mental health services was higher for women with 3 or more comorbid disorders (65%) than for those with no disorder (9.8%), or only 1 disorder (51.4%). CONCLUSIONS: The results of this study highlight the need to conduct more research into the reasons for the low rates of professional service use, especially for women with high comorbidity. They also highlight the need to understand the phenomenon underlying the possibly increasing rates of disorders in younger birth cohorts, so that outreach strategies can be modified to accommodate differences in younger women.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".