Exploring Coping Strategies of Persons with Mental Illness in Ghana: A Synthesis of the Qualitative Literature
Bibliographic record
Abstract
Evidence points to the increasing prevalence of mental illness in Ghana. Yet, research to understand the strategies used to cope with mental illness is lacking in the Ghanaian context, where psychiatric care is limited. The aim of this review is to identify and synthesize existing qualitative evidence on the strategies adopted by persons with mental illness to manage stress. We conducted the scoping review using the Arksey and O’Malley framework. A search of published qualitative studies on mental illness in Ghana between 2000 and 2019 using Scopus, Embase, Medline, and PsycINFO was conducted. Nine articles met our inclusion criteria. Based on Skinner and colleagues’ typology of coping strategies, we categorized the coping strategies into five domains: problem solving , support seeking, avoidance , distraction, and positive cognitive restructuring. Faith-based healing and prayers were the most common coping strategies identified in the review. Other strategies included seeking biomedical care, maintaining positive relationships, substance use, listening to music, and isolation. The review calls for a coordinated mental healthcare provision and the need for increased research on mental illness in Ghana.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".