Mental health care seeking behavior in Bangladesh: determinants and treatment gaps
Bibliographic record
Abstract
BACKGROUND: The study provides evidence of the existing pattern of mental healthcare-seeking behavior and treatment gaps among the adult population in Bangladesh and identifies the factors associated with mental healthcare-seeking behavior in the country. METHODS: We used the National Mental Health Survey Bangladesh 2019 dataset with 7270 households to identify the patterns of and facilitators for seeking mental health care in Bangladesh. A Probit model using a standard normal cumulative distribution function (CDF) with three specifications has been applied to identify the factors influencing mental healthcare-seeking behavior and the probability of seeking mental healthcare services in Bangladesh. We also compared the probit model results with the logit model (a similar method) to examine the consistency of the findings. RESULTS: The study finds a large treatment gap for mental health care seeking in Bangladesh. We found that about 90% of mental health patients do not seek mental healthcare services in the country. Among the different mental health disorders, addictive disorder is found with the highest treatment gap (95.24%), while bipolar disorder is observed with the lowest treatment gap (65.63%). Both the logit and probit model shows that the existence of a mentally disordered patient in a family is the only statistically significant determinant that increases the probability of seeking mental health care for other family members among the socio-economic factors, such as gender, age, marital status, religion, education level, household size, and residential status. The marginal effects analysis shows that the existence of a mentally disordered family member increases the probability of seeking mental health care services by around 6% both in logit and probit models. By disaggregating the sample observations into women and men, we also found that marital status and household size are significant determinants. The other socio-economic variables considered in the study are found statistically insignificant. The lesser tendency to seek mental health treatment in the country requires policy intervention by government and non-government organizations. CONCLUSION: Though mental health conditions are major public health concerns in Bangladesh, the treatment-seeking behavior among people with mental health disorders is very low, implying a large treatment gap for mental health conditions. The findings indicate the urgent need to increase mental health service coverage among mental health patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".