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Extending “Helping Babies Breathe” to an Academic Setting in Fiji: A Project Involving American Nursing Faculty and Graduate and Undergraduate Nursing Students

2021· article· en· W3196626862 on OpenAlex
Janelle L. B. Macintosh, Ashley L. Ferrara, Gaye Ray, Karlen E. Luthy, Renea L. Beckstrand

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueNursing Education Perspectives · 2021
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsnot available
Fundersnot available
KeywordsNeonatal resuscitationNursingMedicineMedical educationQuarter (Canadian coin)PsychologyResuscitationEmergency medicine

Abstract

fetched live from OpenAlex

ABSTRACT: Approximately 2.5 million neonates died worldwide in 2018. Over one quarter of neonatal deaths are caused by birth asphyxia. Helping Babies Breathe (HBB) was created to teach basic neonatal resuscitation steps in limited-resource settings. Fifteen Fijian faculty members attended a master teacher class. Nine undergraduate nursing students from the western United States assisted in teaching two HBB classes for Fijian nursing students. Fijian faculty and student knowledge increased significantly posteducation. Educational settings provide ideal locations for future nurses to learn and practice evidence-based neonatal resuscitation skills. Implementing HBB in an academic setting, though novel, may ensure educators are familiar with current guidelines.

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.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
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
Bibliometrics0.0010.001
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 head0.047
GPT teacher head0.443
Teacher spread0.396 · 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