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Record W3182518844

Requirements and costs for scaling up comprehensive emergency obstetric and neonatal care in health centres in Tanzania

2021· article· en· W3182518844 on OpenAlexaff
Angelo Nyamtema, Godfrey Mtey, John C. LeBlanc

Bibliographic record

VenueAfrican Journal of Reproductive Health · 2021
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTanzaniaMedicineMedical emergencyHealth careDeveloping countryScale (ratio)BusinessEnvironmental healthEmergency medicineEconomic growthSocioeconomicsGeography
DOInot available

Abstract

fetched live from OpenAlex

The objective of this study was to identify and determine the costs of essential components of a resource package and strategies for scaling up comprehensive emergency obstetric and neonatal care services in Tanzania. Essential components were identified through lessons learned during implementation of comprehensive emergency obstetric and neonatal care and regular discussions with key stakeholders. The related costs were collected from the health centres, Tanzania Medical Store Department and non-governmental organizations that had upgraded health centres for comprehensive emergency obstetric and neonatal care services provision. The results showed that the estimated costs of upgrading a health centre to provide comprehensive emergency obstetric and neonatal care services was $256,650 (USD) for infrastructure and equipment, $4,463 per person for upgrading skills in either in comprehensive emergency obstetric and neonatal care or anaesthesia for three months and $43,500 per year for medicines and supplies. The total cost for all components per health centre was estimated at $560,802. Scale up required many complementary strategies at all health system levels. Scale up of comprehensive emergency obstetric and neonatal care services in health ccentres in underserved areas is feasible and urgently needed in resource-limited countries. (Afr J Reprod Health 202 1; 25[3s]: 84-91 ).

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.030
GPT teacher head0.337
Teacher spread0.307 · 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 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

Citations2
Published2021
Admission routes1
Has abstractyes

Explore more

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