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Record W2767393711 · doi:10.1377/hlthaff.2017.0548

Innovative Product Development Partnership Reduced Neonatal Mortality In Nepal Through Improved Umbilical Cord Care

2017· article· en· W2767393711 on OpenAlexaff
Peter Oyloe, Leela Khanal, Stephen Hodgins, Sabita T. Pradhan, Penny Dawson

Bibliographic record

VenueHealth Affairs · 2017
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsProvincial Laboratory of Public Health
Fundersnot available
KeywordsUmbilical cordGeneral partnershipProduct (mathematics)MedicineNeonatal mortalityBusinessInfant mortalityObstetricsEnvironmental healthIntensive care medicineEconomic growthEconomicsFinancePopulationImmunology

Abstract

fetched live from OpenAlex

Approximately 40 percent of all newborn deaths in Nepal are attributable to neonatal infections. A randomized controlled trial conducted in Nepal in the period 2002-05 on the application of a solution of the disinfectant chlorhexidine to umbilical cord stumps of newborns showed a reduced risk of infections and death. In response to these results, the Government of Nepal and various partners mobilized to deliver this simple, low-cost intervention on a national scale. We describe the design, development, and maturation of a partnership among the government, technical assistance agencies, and a local pharmaceutical company to create a suitable, commercially available gel product to reduce newborn infections. Essential contributors to the partnership's effectiveness included having a for-profit pharmaceutical company as a fully engaged partner; having responsive, flexible relationships among the partners that evolved over time; and paying attention to competition within the private sector. A less formalized arrangement among partners allowed them to build trust in each other over time. Government stewardship of the program throughout the scale-up process ensured that policy and systems integration were aligned as the program matured.

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)0.000
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 score0.253
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.073
GPT teacher head0.387
Teacher spread0.314 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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