Pattern of psychiatric morbidity among female patients who attended private consultation chambers in Dhaka city
Bibliographic record
Abstract
Mental illness affect women and men differently - some disorders are more common in women; some are manifested with different symptoms. In Bangladesh, 16.1% of the adult populations suffer from some degree of mental disorder and the prevalence is higher in women than men (19.0% vs 12.9%). This study is a cross sectional study, done on female patients who attended at private chambers of psychiatrists located within Dhaka city of Bangladesh. Total sample size is 280 and duration of the study was six months from May 2014 to October 2014. The major objective of the study was to determine the pattern of psychiatric illness among the women who attended some psychiatrists private chamber in Dhaka city and also to identify the socio-economic and environmental stressors causing psychiatric illness. The findings revealed that highest numbers of the patients (41%) belonged to the age group between 21-30 years and the second largest group having 31-40 years of age (21%). Most patients hailed from urban area (79%) and among all the patients most of them were married (58%). There are various psycho-social stressors which can be held responsible for causing psychiatric illness - domestic violence, marital breakdown and co-morbid physical illness. Among the several pattern of mental diseases, depressive disorder was the commonest (17.7%), followed by somatoform disorder (14%) and schizophrenia (13.3%). Among all the patients, substance abuse was found in 4.2% of patients. This study finally argues that for reduction of psychiatric morbidity among female patients, medical services must be extended to the community level.Bangladesh Med J. 2016 Jan; 45 (1): 14-19
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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 | 0.000 | 0.001 |
| 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) | 0.004 | 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".