Effects of demographic and socio-economic factors on dissatisfaction with formal healthcare utilisation among older adults with very low incomes in Ghana
Bibliographic record
Abstract
The older adult population has increased in the past few decades, and this is coupled with an increasing burden of chronic non-communicable diseases and a higher rate of healthcare utilisation among this population group. However, studies on healthcare utilisation have not tackled the healthcare service use dissatisfaction dynamics and associated factors, especially among older adults with very low incomes surviving under a social protection programme. The purpose of this study was to estimate demographic and socio-economic factors driving dissatisfaction with the utilisation of formal healthcare services among older adults with very low incomes in Ghana. Using data from a larger Ageing, Health, Lifestyle and Health Services survey conducted in the Atwima Nwabiagya District of Ghana, we estimate dissatisfaction with formal healthcare utilisation using multivariate logistic regression analysis. The study revealed that about 29% of the participants were dissatisfied with utilising formal healthcare services. The study showed that females (adjusted odd ratio (AOR): 0.42, 95% CI: 0.15–0.76) and those with high school education (AOR: 0.35, 95% CI: 0.23–0.79) were significantly less likely to be dissatisfied with the utilisation of formal healthcare services compared with males or those with no formal education. We found that participants who earned a monthly income of GH¢201 or more were significantly more likely to be dissatisfied with the utilisation of formal healthcare services than those who earned less than GH¢100 (AOR:1.16, 95% CI: 1.06–3.94). Our findings provide evidence that few older adults with very low incomes are dissatisfied with using formal healthcare services. The study has shown that demographic and socio-economic factors, particularly, gender, income, marital status, and education, are significant drivers of dissatisfaction with formal healthcare utilisation among older adults with very low incomes. This study, therefore, has implications for policy, practice, and future research decisions.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".