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Record W2165681434 · doi:10.1177/0894318413500403

Building the Nightingale Initiative for Global Health—NIGH

2013· article· en· W2165681434 on OpenAlexaff
Deva‐Marie Beck, Barbara M. Dossey, Cynda Hylton Rushton

Bibliographic record

VenueNursing Science Quarterly · 2013
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsCentre for Global Health Research
Fundersnot available
KeywordsPolitical scienceMedicine

Abstract

fetched live from OpenAlex

The Nightingale Initiative for Global Health (NIGH) is a major grassroots-to-global movement of "daring, caring and sharing" of nursing and others around the world inspired by the outstanding legacy of Florence Nightingale. The Nightingale Initiative envisions and emulates what Nightingale might have accomplished if she lived in the digital age and with international agencies such as the United Nations and World Health Organization. It challenges nurses everywhere to think and act both locally and globally, to raise their voices about the contribution of nursing, and to become authentic advocates, particularly in addressing the United Nations Millennium Development Goals.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0100.009
Scholarly communication0.0100.014
Open science0.0020.027
Research integrity0.0060.011
Insufficient payload (model declined to judge)0.0260.006

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 head0.022
GPT teacher head0.369
Teacher spread0.347 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations20
Published2013
Admission routes1
Has abstractyes

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Same venueNursing Science QuarterlySame topicGlobal Maternal and Child HealthFrench-language works237,207