Climate change impacts and mental health in poor urban coastal communities in Ghana
Bibliographic record
Abstract
Coastal communities in African countries with lower carbon emissions face greater climate challenges but lack the capacity to address these challenges. The implication is that these communities suffer more from the impacts that climate change brings. Despite sustained research efforts into climate impacts on such communities, the mental health aspects of these impacts are often overlooked. In this study, we explored the lived experiences of climate-related mental health challenges and community coping mechanisms within three poor urban coastal communities in Ghana, West Africa. Fifty-seven community members participated in the study. Data were collected through five focus group discussions and fifteen one-on-one in-depth interviews. Thematic Network Analysis was used to analyse the data. Results showed that rising sea levels have caused loss of livelihoods and properties, which in turn have exacerbated mental health challenges within the communities. Community members' coping mechanisms include the use of techno-managerial interventions, relocation, spirituality, and social support. The findings contribute to the broader discourse on climate change and its multifaceted consequences, highlighting the interconnectedness of environmental and mental health challenges in coastal landscapes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".