The COVID-19 pandemic and dynamics of livelihood assets in the Kwahu South District of Ghana: determinants and policy implications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it