Health system preparedness for integration of mental health services in rural Liberia
Bibliographic record
Abstract
BACKGROUND: There are increasing efforts and attention focused on the delivery of mental health services in primary care in low resource settings (e.g., mental health Gap Action Programme, mhGAP). However, less attention is devoted to systematic approaches that identify and address barriers to the development and uptake of mental health services within primary care in low-resource settings. Our objective was to prepare for optimal uptake by identifying barriers in rural Liberia. The country's need for mental health services is compounded by a 14-year history of political violence and the largest Ebola virus disease outbreak in history. Both events have immediate and lasting mental health effects. METHODS: A mixed-methods approach was employed, consisting of qualitative interviews with 22 key informants and six focus group discussions. Additional qualitative data as well as quantitative data were collected through semi-structured assessments of 19 rural primary care health facilities. Data were collected from March 2013 to March 2014. RESULTS: Potential barriers to development and uptake of mental health services included lack of mental health knowledge among primary health care staff; high workload for primary health care workers precluding addition of mental health responsibilities; lack of mental health drugs; poor physical infrastructure of health facilities including lack of space for confidential consultation; poor communication support including lack of electricity and mobile phone networks that prevent referrals and phone consultation with supervisors; absence of transportation for patients to facilitate referrals; negative attitudes and stigma towards people with severe mental disorders and their family members; and stigma against mental health workers. CONCLUSIONS: To develop and facilitate effective primary care mental health services in a post-conflict, low resource setting will require (1) addressing the knowledge and clinical skills gap in the primary care workforce; (2) improving physical infrastructure of health facilities at care delivery points; and (3) implementing concurrent interventions designed to improve attitudes towards people with mental illness, their family members and mental health care providers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".