Family Support and Stigma among Patients Attending a Tertiary Care Hospital in Bangladesh
Bibliographic record
Abstract
Background: Mental illness stigma continues to be a major barrier for individuals with mental illness.Methods: This study was a descriptive cross-sectional study conducted among the mentally ill patients attending Out Patient Department (OPD) in a tertiary care hospital of Dhaka city of Bangladesh, during the period from May 2013 to November 2013. The objective of the study was to assess the family support and stigma among the respondents. A total of 151 patients of 18 to 60 years attending in the OPD, National Institute of Mental Health (NIMH), Dhaka were selected purposefully to conduct the study. An informed consent was taken from the patients or care givers. Information about socio-demographic and psychosocial data was collected using the questionnaire designed by the researcher based on Factors Influencing Neuroleptic Medication Taking Scale (FNIMTS). Diagnoses of mental illness were done according to Diagnostic and Statistical Manual of Mental Disorders Text Revision (DSM-IV-TR) by psychiatrists.Results: The commonest diagnoses among the respondents were the schizophrenias (56.3%), bipolar mood disorders (23.8%) and substance related disorder (6.6%). Most were insightful into their mental illness (89.4%). Most of the respondents rated their perception of family support as good (66.2%). Most of the respondents believed that they would be treated differently if people knew they had a mental illness (57.6%) or on drugs for mental illness (54.3%). Among the presently employed respondents (21.85%) most (57.75%) were uncomfortable with the idea of telling employer about mental illness or being on drugs for it.Conclusion: A bridge for communication between people with mental illness, their families, and health care practitioners, may be a useful framework for guiding efforts to reduce stigma. Community-based participatory research principles and lived experiences are crucial elements in stigma reduction endeavors.Birdem Med J 2017; 7(2): 148-154
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".