The COVID-19 pandemic and dynamics of livelihood assets in the Kwahu South District of Ghana: determinants and policy implications
Bibliographic record
Abstract
The debate on the changes in livelihood assets as a function of health shocks remains inconclusive, thus spurring attention from scientists and development practitioners across the globe. This paper analyses COVID-19-induced changes in the livelihood assets of rural households in Ghana. While the content analysis was employed in qualitative data analysis, the quantitative data set was analysed using the binary logistic regression model. The analyses led to the following conclusions: The COVID-19 pandemic led to a more significant decline in financial assets than social assets. Although several socio-economic factors determine changes in the livelihood assets of households, the assets base of migrants was disproportionately affected by the pandemic. Also, women were disproportionately affected since market access restrictions significantly affected their income and savings and, consequently, their ability to buy farm necessitites. These results suggest the need to emphasise the resilience of financial assets in times of pandemics, especially for migrants. This study provides new insights to inform the sustainable livelihoods framework, emphasising pandemics and changing livelihood strategies. Studies to uncover the coping strategies of migrants in the context of health shocks are required to complement this position.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".