Disability and common mental disorders: Results from the World Mental Health Survey Initiative Portugal
Bibliographic record
Abstract
BACKGROUND: Common mental disorders are highly prevalent and disabling, leading to substantial individual and societal costs. This study aims to characterize the association between disability and common mental disorders in Portugal, using epidemiological data from the World Mental Health Survey Initiative. METHODS: Twelve-month common mental disorders were assessed with the CIDI 3.0. Disability was evaluated with the modified WMHS WHODAS-II. Logistic regression models were used to assess the association between disability and each disorder or diagnostic category (mood or anxiety disorders). RESULTS: Among people with a common mental disorder, 14.6% reported disability. The specific diagnoses significantly associated with disability were post-traumatic stress disorder (OR: 6.69; 95% CI: 3.20, 14.01), major depressive disorder (OR: 3.49; 95% CI: 2.13, 5.72), bipolar disorder (OR: 3.41; 95% CI: 1.04, 11.12) and generalized anxiety disorder (OR: 3.14; 95% CI: 1.43, 6.90). Both categories of anxiety and mood disorders were significantly associated with disability (OR: 1.88; 95% CI: 1.23, 2.86 and OR: 3.94; 95% CI: 2.45, 6.34 respectively). CONCLUSIONS: The results of this study add to the current knowledge in this area by assessing the disability associated with common mental disorders using a multi-dimensional instrument, which may contribute to mental health policy efforts in the development of interventions to reduce the burden of disability associated with common mental disorders.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".