Utilisation of formal healthcare and associated factors: evidence from older people with functional limitations in Ghana
Bibliographic record
Abstract
Literature suggests that ageing is associated with healthcare utilisation. Even though functional limitations among older adults have been studied, there is a lack of literature exploring the demographic profile concerning their formal healthcare utilisation in Ghana. This paper evaluates the effect of socio-demographic variables on the use of formal healthcare among older adults with functional limitations. Using a quantitative approach, a survey was conducted using questionnaires to gather data from 394 participants in the Greater Kumasi Metropolitan Area in Ghana. Logistic regression models were used to determine the drivers of the use of formal healthcare. The study revealed that 57.6% of older adults with functional limitations utilised formal healthcare. The determinants of formal healthcare utilisation among older adults were males (AOR: 1.97; CI: 1.12–3.45, p = 0.019), living with a spouse and children (AOR: 2.94; CI: 1.31–6.59, p = 0.009), perceived poor health status (AOR: 1.86; CI: 1.00–3.44, p = 0.050), active national health insurance scheme (AOR: 6.18; CI: 3.54–10.79, p = 0.000), chronic diseases (AOR: 3.29; CI: 1.70–6.40, p < 0.001), travelled 30 minutes or more to access healthcare (AOR: 2.08; CI: 1.03–4.18, p = 0.041) and waiting times of 60 minutes or more at a health facility (AOR: 4.61; CI: 2.06–10.35, p < 0.001). Our study suggests the importance of integrating gender-related and health system-specific strategies into healthcare policy decisions in Ghana. Based on these findings, we conclude that reducing waiting times and improving access to the national health insurance scheme may enhance the utilisation of formal healthcare services for older adults with functional limitations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".