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Record W2105725949 · doi:10.1177/1010539515603447

Does Resuscitation Training Reduce Neonatal Deaths in Low-Resource Communities? A Systematic Review of the Literature

2015· review· en· W2105725949 on OpenAlexaff
Sarah Sousa, John G. Mielke

Bibliographic record

VenueAsia Pacific Journal of Public Health · 2015
Typereview
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsResuscitationMedicineNeonatal resuscitationAsphyxiaPsychological interventionIntervention (counseling)Relative riskDeveloping countryIntensive care medicineEmergency medicineMedical emergencyNursingPediatrics

Abstract

fetched live from OpenAlex

Every year, nearly 1 million babies succumb to birth asphyxia (BA) within the Asia-Pacific region. The present study sought to determine whether educational interventions containing some element of resuscitation training would decrease the relative risk (RR) of neonatal mortality attributable to BA in low-resource communities. We systematically reviewed 3 electronic databases and identified 14 relevant reports. For community deliveries, providing traditional birth attendants (TBAs) with neonatal resuscitation training modestly reduced the RR in 3 of 4 studies. For institutional deliveries, training a range of clinical staff clearly reduced the RR within 2 of 8 studies. When resuscitation-specific training was directed to community and institutional health care workers, a slight benefit was observed in 1 of 2 studies. Specific training in neonatal resuscitation appears most effective when provided to TBAs (specifically, those presented with ongoing opportunities to review and update their skills), but this particular intervention alone may not appreciably reduce mortality.

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)0.035
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.424
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0350.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.004
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 head0.173
GPT teacher head0.439
Teacher spread0.266 · 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; both teacher heads agree on what is shown here.

Study designSystematic review
Domainnot available
GenreReview

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

Citations8
Published2015
Admission routes1
Has abstractyes

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