Family Support, Perceived Physical Activeness and Chronic Non-Communicable Diseases as Determinants of Formal Healthcare Utilization Among Older Adults with Low Income and Health Insurance Subscription in Ghana
Bibliographic record
Abstract
Evidence suggests that enrollment in a health insurance scheme is associated with higher levels of formal healthcare utilization among older adults, especially those with low income in sub-Saharan Africa (SSA), including Ghana. This study examines the prevalence of formal healthcare utilization and associated factors among older adults with low income and health insurance subscription enrolled in a social intervention program (known as the Livelihood Empowerment Against Poverty [LEAP] program) in Ghana. Cross-sectional data were obtained from an Aging, Health, Lifestyle and Health Services Survey conducted in 2018 among 200 older adults aged 65 years and above enrolled in the LEAP program. The results showed that almost 9 in 10 (87%) older adults utilized formal healthcare services for their health problems. Older adults who received family support, rated themselves to be physically active and had non-communicable diseases (NCDs) were more significantly likely to utilise formal health care services than their counter parts. We recommend that health policies and programs for older adults with low income and health insurance subscription under the LEAP program should consider the roles of family support, physical activeness and NCDs in influencing their use of formal healthcare services.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".