Burden of common mental disorders in a community health centre sample.
Bibliographic record
Abstract
OBJECTIVE: To examine the rates of common mental disorders (CMDs) such as depression, anxiety, posttraumatic stress disorder (PTSD), and alcohol use in an urban community health care centre (CHC) serving vulnerable immigrant and ethnoracial communities in order to improve knowledge on the rates of CMDs specific to these groups accessing primary care settings. DESIGN: English or Spanish, self-administered, tablet-based survey known as the Interactive Computer-Assisted Client Assessment Survey (iCCAS). SETTING: Access Alliance Multicultural Health and Community Services CHC in Toronto, Ont. PARTICIPANTS: Adult patients waiting to see a clinician. MAIN OUTCOME MEASURES: The iCCAS screened for depression (using the PHQ-9 [Patient Health Questionnaire]), anxiety (using the GAD-7 [Generalized Anxiety Disorder 7-item scale]), PTSD (using the PC-PTSD [Primary Care PTSD Screen]), and alcohol dependency (using the CAGE questionnaire); those with an existing diagnosis and active treatment for one of these conditions were not asked to complete that condition-specific screening scale. An exit survey measured demographic characteristics and relevant indicators. RESULTS: A response rate of 78.6% was achieved. The iCCAS survey was completed by 75 patients (26 men and 49 women) with a mean age of 36.5 years. Almost all were first-generation immigrants: 32.0% originated from Latin America, 28.0% from South Asia, and 17.3% from Africa or the Middle East. Major depression was found among 44.0% of participants (11 with diagnosis and treatment, 22 with a score of 10 or greater on the PHQ-9). Generalized anxiety disorder was present in 26.7% of participants (7 with diagnosis and treatment, 13 with a score of 10 or greater on the GAD-7 scale). Posttraumatic stress disorder was detected in 37.3% of participants (7 with diagnosis and treatment, 21 with a score of 3 or greater on the PC-PTSD tool). Alcohol dependency was found among 10.7% of participants (1 with diagnosis and treatment, 7 with a score of 2 or greater on the CAGE questionnaire). CONCLUSION: The high rates of probable depression, generalized anxiety, and PTSD that were found in the studied population suggest a need for systematic assessment of CMDs in CHCs, as well as training and resources to increase readiness to handle identified cases.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".