Barriers to mental health service utilisation in Sudan - perspectives of carers and psychiatrists
Bibliographic record
Abstract
BACKGROUND: In order to scale up mental health care nationally, barriers to health services utilisation need to be identified and addressed. AIM: The aim of this study was to identify barriers to mental health services utilization in Sudan from the perspectives of carer's of mentally ill patients and psychiatrists and to make recommendations to address the identified barriers. METHODS: Mixed research methods were used in this cross sectional survey. The quantitative part was conducted with carers of mentally ill patients who were staying in Tijani Elmahi psychiatric hospital in Sudan, and the qualitative part was conducted with the psychiatric consultants in the country. RESULTS: 103 carers and six psychiatric consultants participated in the study. According to carers, the main barriers to utilisation of mental health services includes: the beliefs around mental illness, resorting to alternative treatments such as religious and traditional healers, centralization of mental health services, inadequate number of mental health staff, and mental health not being a priority by policy makers. In addition to these barriers, the psychiatric consultants identified stigma, cost of medications, and worries about medication's side effects as barriers to the utilisation of mental health services. The carers and psychiatrists proposed several solutions to address the barriers to health services utilisation. CONCLUSION: Carers and psychiatrists are aware of the barriers to mental health services utilisation in Sudan. Addressing these barriers require a health policy and political response.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".