Pathways and access to mental health care services by persons living with severe mental disorders and epilepsy in Uganda, Liberia and Nepal: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Access to mental health care services for patients with neuropsychiatric disorders remains low especially in post-conflict, low and middle income countries. Persons with mental health conditions and epilepsy take many different paths when they access formal and informal care for their conditions. This study conducted across three countries sought to provide preliminary data to inform program development on access to care. It thus sought to assess the different pathways persons with severe mental disorders and epilepsy take when accessing care. It also sought to identify the barriers to accessing care that patients face. METHODS: Six in depth interviews, 27 focus group discussions and 77 key informants' interviews were conducted on a purposively selected sample of health care workers, policy makers, service users and care takers in Uganda, Liberia and Nepal. Data collected along predetermined themes was analysed using Atlas ti software in Uganda and QSR Nvivo 10 in Liberia and Nepal RESULTS: Individual's beliefs guide the paths they take when accessing care. Unlike other studies done in this area, majority of the study participants reported the hospital as their main source of care. Whereas traditional healers lie last in the hierarchy in Liberia and Nepal, they come after the hospital as a care option in Uganda. Systemic barriers such as: lack of psychotropic medicines, inadequate mental health specialists and services and negative attitudes of health care workers, family related and community related barriers were reported. CONCLUSION: Access to mental health care services by persons living with severe mental disorders and epilepsy remains low in these three post conflict countries. The reasons contributing to it are multi-faceted ranging from systemic, familial, community and individual. It is imperative that policies and programming address: negative attitudes and stigma from health care workers and community, regular provision of medicines and other supplies, enhancement of health care workers skills. Ultimately reducing the accessibility gap will also require use of expert clients and families to strengthen the treatment coalition.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".