Coping strategies adapted by Ghanaians during the COVID-19 crisis and lockdown: A population-based study
Bibliographic record
Abstract
The COVID-19 pandemic and control measures adopted by countries globally can lead to stress and anxiety. Investigating the coping strategies to this unprecedented crisis is essential to guide mental health intervention and public health policy. This study examined how people are coping with the COVID-19 crisis in Ghana and identify factors influencing it.This study was part of a multinational online cross-sectional survey on Personal and Family Coping with COVID-19 in the Global South. The study population included adults, ≥18 years and residents in Ghana. Respondents were recruited through different platforms, including social media and phone calls. The questionnaire was composed of different psychometrically validated instruments with coping as the outcome variable measured on the ordinal scale with 3 levels, namely, Not well or worse, Neutral, and Well or better. An ordinal logistic regression model using proportional odds assumption was then applied.A total of 811 responses were included in the analysis with 45.2% describing their coping level as well/better, 42.4% as neither worse nor better and 12.4% as worse/not well. Many respondents (46.9%) were between 25-34 years, 50.1% were males while 79.2% lived in urban Ghana. Having pre-existing conditions increased the chances of not coping well (aOR = 1.86, 95%CI: 1.15-3.01). Not being concerned about supporting the family financially (aOR = 1.67, 95%CI: 1.06-2.68) or having the feeling that life is better during the pandemic (aOR = 2.37, 95%CI: 1.26-4.62) increased chances of coping well. Praying (aOR: 0.62, 95%CI: 0.43-0.90) or sleeping (aOR: 0.55, 95%CI: 0.34-0.89) more during the pandemic than before reduces coping.In Ghana, during the COVID-19 pandemic, financial security and optimism about the disease increase one's chances of coping well while having pre-existing medical conditions, praying and sleeping more during the pandemic than before reduces one's chances of coping well. These findings should be considered in planning mental health and public health intervention/policy.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".