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Record W2135392385

Factors Influencing PhilHealth Coverage and In-patient Benefit Utilization of Filipino Children under Five

2013· preprint· en· W2135392385 on OpenAlexaboutno aff
Elizabeth María, Angeline D. Puyat

Bibliographic record

VenueRePEc: Research Papers in Economics · 2013
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsLogistic regressionDescriptive statisticsEnvironmental healthPaymentHealth careHealth insurancePopulationBusinessQuarter (Canadian coin)MedicineDemographyGeographyEconomic growthEconomicsFinance
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.069
GPT teacher head0.308
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2013
Admission routes1
Has abstractyes

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