[Common mental disorders and use of psychotropic medications in women consulting at primary care units in a Brazilian urban area].
Bibliographic record
Abstract
OBJECTIVE: To investigate the prevalence of common mental disorders (CMD) in women consulting at primary health care units in a Brazilian urban area, as well as to determine the impact of CMD on quality of life (QoL), the association of sociodemographic factors with CMD and QoL, and the prevalence and pattern of psychotropic medication use in the study sample. METHODS: In this quantitative, cross-sectional, correlational-descriptive study, a stratified sample of 365 women was interviewed between May 2012 and January 2013 in five primary health care units in Brazil. Data were collected using sociodemographic and drug use questionnaires. The self-reporting questionnaire (SRQ-20) was used to estimate the prevalence of CMD; and the World Health Organization Quality of Life (WHOQOL)-bref instrument was used to assess quality of life. To evaluate the impact of CMD on QoL, the t test and linear regression models were employed. The chi-square test was used to verify associations between CMD and sociodemographic variables. Descriptive analysis was used for psychotropic drug use. RESULTS: The prevalence of CMD was 44.1%. The prevalence of psychotropic medication use was 27.1%. Only 5.6% of participants had a psychiatric diagnosis recorded in their medical chart. Psychotropic drugs were used by 41.6% of participants with CMD according to the SRQ-20 and by 15.7% of those without CMD. There was no association between CMD and sociodemographic variables, but CMD and QoL were significantly associated. Women with CMD had the worst QoL, without impact of sociodemographic variables. CONCLUSIONS: Further attention should be given to the pattern of psychotropic medication use and prescription in primary care settings. Women with CMD had functional impairment, as shown by significantly lower QoL scores. The fact that sociodemographic factors did not impact the present results seems to support the notion of a different basis for CMD in 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 | 0.001 |
| 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) | 0.002 | 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".