Factors Influencing PhilHealth Coverage and In-patient Benefit Utilization of Filipino Children under Five
Bibliographic record
Abstract
According to the 2008 National Demographic and Health Survey (NDHS) report, children under 5 are more likely to use in-patient care than other age groups. These children are not only more vulnerable to getting sick, but are also at risk of incurring high health expenditures if they are without health insurance. Using the 2008 NDHS dataset, this study focused on the coverage and in-patient benefit utilization of children under 5, who are dependents of the Philippine Health Insurance Corporation (PhilHealth). Unique to this analysis was the shift in focus of coverage and utilization from the traditional angle of primary members to the dependents. Descriptive analyses revealed that PhilHealth covered only 33.93 percent of the under-5 population, and of those PhilHealth dependents who were confined in a hospital, 67.59 percent used PhilHealth as a source of payment. Logistic regression analysis determined that age and educational attainment of the household head, region, and wealth index were significant factors that influenced coverage. Moreover, it was found that confinement in a private facility and for longer periods of time increased the probability of in-patient benefit utilization for PhilHealth dependents. These results will be useful for PhilHealth as they create evidence-based initiatives to attain Universal Health Coverage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".