Social domains of poor mental health: A qualitative pilot study of community stakeholders’ understanding and demarcation of mental illness and its interpretations in rural Nigeria
Bibliographic record
Abstract
Background and Aims: Although previous studies on mental health/illness in Nigeria have explored knowledge and attitude of community members using quantitative approaches, few studies have engaged stakeholders within rural communities on the issue of mental illness using qualitative approaches. Community stakeholders play a critical role in influencing health behaviors. The objective of this pilot study was to explore community stakeholders' understanding and demarcation of mental illness, and its interpretations in a rural Nigerian town. This is with the aim of shaping stakeholders understanding of people when they experience mental distress within the community. Methods: The study was conducted in Ijebu-Igbo town of Ogun State in south-west Nigeria. In-depth interviews were conducted among two religious' leaders: a Pastor and an Imam, a traditional healer, a medical doctor, and a registered nurse, and a focus group discussion was held in a church with members of its advisory committee. Results: The findings showed that community stakeholders gave multiple interpretations of mental illness and many attribute mental illness to spiritual attack, ancestral curse, anger of the gods, and personal affliction (Ogun-Afowofa). This has been categorized as familial and individual attributes in this study which is part of the main themes derived. The study findings also show that the understanding of community members regarding the root causes of mental illness is somewhat vague based on their poor knowledge of mental illness. This is because of the various interpretations they gave to explain mental health is based on their cultural orientation, socialization, and belief system, and not based on any medical knowledge. Conclusion: This pilot study was conducted to justify the main study. There is therefore a need for health education interventions to enlighten and educate community stakeholders with requisite knowledge for better understanding and interpretation of mental illness. Also, through mental health education interventions, community members will gain clarity on what mental health is and what it is not.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".