When little can do more: the case for investing in mental healthcare in Ghana
Bibliographic record
Abstract
About 21% of adults in Ghana suffer from moderate-severe psychological distress, leading to unemployment and productivity losses to 7% of Ghana´s gross domestic product (GDP) [1]. In two recently published reports on mental healthcare in Ghana the World Health Organization- Assessment Instrument for Mental Health Systems (WHO-AIMS) 2020 and the World Health Organization special initiative for mental health situational assessment 2021 [2,3], key mental health considerations of public health and policy relevance draw our attention. Despite the significant strides Ghana has achieved over the years, the goal of creating an effective and comprehensive mental health service delivery infrastructure and workforce is far from complete. For instance, the current mental health service delivery architecture is skewed, with the Southern part advantaged. All three psychiatric hospitals in Ghana are in the South, with only three out of about sixty psychiatrists working in small psychiatric departments in the North. As a result, indigenes have to travel over 700 km by road to seek care. This calls for attention to the equality and equity dimensions of health planning. The three psychiatric hospitals in Ghana have a combined capacity of 3.8 beds per 100,000 [3]. Additionally, whilst some low- and middle-income countries (LMICs) are investing about 4.0% of health expenditure in mental health, Ghana is investing 3% [4]. Another major challenge is low treatment coverage for mood disorders, with only 0.61% of persons with major depressive disorder receiving treatment. This rate is lower than the average of 16.8%-21.4% for some low-middle-income countries [5]. Furthermore, there are numerous challenges with the availability of psychotropic medications. This is a massive setback because Ghana has an overreliance on medical treatments due to a shortage of human resources to provide psychosocial services. In addition, most medications are paid out-of-pocket because Ghana´s national health insurance scheme does not cover them. Additionally, only 7% of health research in Ghana is specific to mental health. Furthermore, the data submitted to the government from health facilities are of low quality [2] and thus unreliable for research purposes, posing significant challenges to the quality and generalizability of the research output.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".