Bibliographic record
Abstract
Nigeria with an estimated $350 per capital annually still ranks near the bottom 158 out of 177 countries in the UN Human Capital Development Index in terms of per capita income, with more than half of the population living in poverty. Over the past decade U5MR is estimated to be 201 deaths/1000 lives births,the high rates of child mortality especially the 0-5 years shows the total breakdown of social and economic well-being of the country .This paper examined child health care demand in Nigeria using the Nested Multinomial Logit Model estimation technique.The study used parents' education as a proxy for child education,while the decision to make a choice of the health facilities was also assumed to be that of the House-Hold head. The study found out that female child has a higher probability of seeking health care facility ahead of their male counterpart. Also, the household head educational level was found to be a determinant of health care seeking behavior of the child. Empirical evidence also revealed that that the probability of seeking healthcare increases with household size and that demand for child health care in Nigeria is non linear in nature.Based on this, the paper recommends the need to show greater commitment to child health care and that government should reduce the problems militating against effective performance of the health sector such as, inefficiency, wasteful use of resources, low quality of service and poor enabling environment.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".