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Record W4322755745 · doi:10.26633/rpsp.2023.31

A commentary on the Pan American Network of Nursing and Midwifery Collaborating Centres

2023· article· en· W4322755745 on OpenAlexafffund
Madeline A. Naegle, Andrea Baumann, Danielle Denwood

Bibliographic record

VenueRevista Panamericana de Salud Pública · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsMcMaster UniversityCentre for Global Health Research
FundersUniversity of North Carolina at Chapel HillUniversity of Illinois at Urbana-ChampaignPontificia Universidad Católica de ChileUniversidad de ChileUniversidade de São PauloUniversity of PennsylvaniaYork UniversityMcMaster UniversityJohns Hopkins UniversityUniversidad Nacional Autónoma de MéxicoUniversity of Miami
KeywordsObstetricsNursingMedicine

Abstract

fetched live from OpenAlex

This article provides a commentary on the Pan American Network of Nursing and Midwifery Collaborating Centres (PANMCC). The objectives are to present an overview of the formation and evolution of the network, its impact on education, research, policy and communication and the benefits of membership. The advantages of international networks as a mechanism to strengthen nursing and midwifery workforces and improve health systems are also highlighted. The Pan American Health Organization (PAHO), the World Health Organization (WHO) Office in the Americas, oversees collaborating centres in the Region. Established in 1999, PANMCC consists of 17 centres situated in universities and schools of nursing. These centres provide crucial nursing and midwifery input to PAHO/WHO. The network supports global engagement and capacity building via collaboration, resource sharing and research colloquia. The linkages within the network enhance professional development, increase capacity building and heighten visibility of PANMCC and the work of its members.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.052
GPT teacher head0.420
Teacher spread0.367 · 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 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

Citations3
Published2023
Admission routes2
Has abstractyes

Explore more

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