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Record W2334969308 · doi:10.5430/jnep.v6n8p123

Gaining a global perspective on public health through an international student nurse collaboration

2016· article· en· W2334969308 on OpenAlexvenueno aff
Donna M. Greenwood, Michelle Honey, Anne Clancy

Bibliographic record

VenueJournal of Nursing Education and Practice · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsnot available
Fundersnot available
KeywordsSolidarityPerspective (graphical)NursingNurse educationPublic healthMedicineMedical educationPsychologyPolitical science

Abstract

fetched live from OpenAlex

Recognizing that nursing practice worldwide will be strongly influenced and shaped by new technologies and their applications, nurse educators from three countries, the United States, New Zealand and Norway collaborated and designed a learning and international networking opportunity for student nurses. The purpose of the project was to enhance awareness of global health, increase student nurses knowledge of public health nursing practices and needs in other countries and to promote solidarity and opportunities for collaboration across boundaries. Nursing students from each country used video conferencing in small groups to meet peers virtually as part of the public health nursing component of their undergraduate course. Experience in collaborating and increased awareness of the differences and similarities of nursing in different contexts was appreciated by the students and the recommendation is that despite some small technical and practical issues, this initiative for international collaboration between student nurses should continue, and other Schools of Nursing could follow this model.

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)0.012
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0080.006
Scholarly communication0.0130.009
Open science0.0010.023
Research integrity0.0030.009
Insufficient payload (model declined to judge)0.0080.001

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 head0.198
GPT teacher head0.586
Teacher spread0.388 · 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 designQualitative
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

Citations1
Published2016
Admission routes1
Has abstractyes

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