Access to financial support services among older adults during COVID-19 pandemic in Ghana
Bibliographic record
Abstract
Background Financial support services are one of the major effective responses to the negative impacts of the COVID-19 pandemic. Yet, there is scant data on financial support services for older adults during the COVID-19 pandemic in Ghana and the factors associated with access to such assistance. Thus, this study sought to address this knowledge gap. Methods The study utilised data on 474 older adults aged 50+ from a coronavirus-related health literacy (CHL) survey conducted in the Ashanti Region of Ghana. We performed multivariate logistic regression analysis to determine the demographic and socio-economic factors associated with access to financial support services among older adults during the COVID-19 in Ghana. Results Out of the 474 Ghanaian older adults sampled, 37.3% received financial support from someone in and/or outside their household during the COVID-19 pandemic. However, after adjusting for the demographic and socio-economic factors, older adults aged 70-79 years (adjusted odds ratio, aOR=0.23, 95% confidence interval, CI=0.12-0.43, P <0.001), those with secondary education (0.33 [0.14-0.82], P =0.016) and those employed (0.51 [0.31-0.85], P =0.009) had lower odds of having access to financial support services from someone in and/or outside their household during the COVID-19 pandemic. Conclusions The demographic and socio-economic factors, particularly age, education and employment status play a critical role in older adults’ access to financial support services during difficult situations. However, the lower prevalence of access to financial support services among older adults could impact older Ghanaians’ welfare and mental health during the COVID-19 pandemic. This highlights the need for the government and welfare institutions to increase the coverage of social welfare programs and packages to include most vulnerable group of older populations who are negatively affected by the COVID-19 pandemic.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".