Health-seeking behaviour during times of illness: a study among adults in a resource poor setting in Ghana
Bibliographic record
Abstract
The implementation of the National Health Insurance Scheme (NHIS) in Ghana aims to bridge the gap between the poor and rich in health-care access and utilization. Guided by Andersen's behavioural model of health services utilization, we examine the factors that influence health-care services utilization in a resource poor setting. Data for the study were obtained through randomly selected respondents in our study location (n = 1137). Logistic regression models were fitted to the data to examine the impact of enabling, predisposing and need factors on health-care-seeking behaviour during last illness. Individuals in the poor and poorest wealth quintiles who are enrolled in the NHIS were less likely to seek treatment in a health facility during their last illness compared with individuals in the richest wealth quintile who are enrolled in the NHIS (β = 0.41, ρ < 0.01 and β = 0.45, ρ < 0.05, respectively). Although health insurance is supposed to increase the likelihood of utilizing health services, poor people in our study who are enrolled in the NHIS are still less likely to utilize health services, suggesting that the NHIS has not succeeded in bridging inequalities in health services utilization between the poor and rich.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".