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Record W2906909602 · doi:10.9745/ghsp-d-18-00256

Revisiting the Facility-Based Delivery Rate Formula in the Philippines for Better Local Health Governance and Services

2018· article· en· W2906909602 on OpenAlexaff
Fude Takayoshi, Sakiko Yamaguchi, Amelita M. Pangilinan, Makoto Tobe, Shogo Kanamori

Bibliographic record

VenueGlobal Health Science and Practice · 2018
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsMcGill University
Fundersnot available
KeywordsResidenceMeasure (data warehouse)Catchment areaCorporate governanceHealth servicesPopulationHealth facilityPlace of birthGeographyLocal governanceBusinessEnvironmental healthDemographyMedicineComputer scienceCartographySociologyFinanceDrainage basin

Abstract

fetched live from OpenAlex

When calculating local facility-based delivery rates, the standard measure based on place of birth excludes residents9 facility births outside the municipality. In contrast, counting the facility births of all residents—regardless of whether they take place within or outside their home municipality—provides a more accurate population- or residence-based measure of use of services for that catchment area. This residence-based measure offers local governments a better understanding of coverage gaps by taking into account place of residence rather than place of birth.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.914
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.027
GPT teacher head0.378
Teacher spread0.351 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations1
Published2018
Admission routes1
Has abstractyes

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