Financial hardship in settling medical bills among households in a Semi-Urban Community in Northwest Nigeria.
Bibliographic record
Abstract
BACKGROUND: An equitable health care system that responds to the needs of its people is important to break the cycle of poverty and ill-health. However, rising health care cost, and the preponderance of user fees to finance health care have often limited access to needed health services. STUDY DESIGN: A cross-sectional descriptive study design was employed, using a pretested, semi-structured, interviewer-administered questionnaire. RESULTS: The study was carried out among 188 respondents. Majority (88.2%) of the respondents were within the age-group 20-49 years, about two-thirds 63.8% were married and about half (42.8%) had family size between 5 and 9. The study revealed that about a quarter (26.1%) experienced hardship in settling their medical bills. While one-third (31.1%) had to sell their assets, about half (45.2%) had to secure loan while 16.6% had to resort to begging because of hardship encountered in settling the medical bills. Furthermore, of those who sold theirs asset; 46.2% sold their farmlands, 38.5% sold a piece of land, while 16.3% sold their vehicles. CONCLUSION: This study has revealed that inhabitants of Samaru community experience hardship in settling their medical bills. Consequently, innovative strategies like deferment of payment and fee exemption, enrolling into community-based health insurance schemes as well as voluntary contributory health insurance schemes etc need to be considered, in order to alleviate the hardship in settling the medical bills.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".