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Record W3137766222 · doi:10.1111/phn.12891

Public health nurses in Canadian schools: An opportunity to build capacity and nursing scholarship

2021· review· en· W3137766222 on OpenAlexaffabout
Vanessa H. Buduhan, Roberta L. Woodgate

Bibliographic record

VenuePublic Health Nursing · 2021
Typereview
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsUniversity of ManitobaCARE CanadaManitoba Health
Fundersnot available
KeywordsNursingPublic healthPublic health nursingScholarshipSchool nursingMedicineScope (computer science)Government (linguistics)Nurse educationPandemicPolitical scienceCoronavirus disease 2019 (COVID-19)Disease

Abstract

fetched live from OpenAlex

Public health nurses (PHNs) in Canada have the potential to significantly benefit the health and academic outcomes of school age children with its impact lasting into adulthood. Unfortunately, cuts in government funding over the years have diminished the presence of PHNs in schools and their ability to practice to their full scope. In the midst of a pandemic, having a strong nursing presence in schools may facilitate public health efforts and reduce the burden on teachers and administration. This discussion paper will explore the current role of nurses in Canadian schools in relation to school nurses in other parts of the world. An overview of the literature looking at the impact of the school nurse on school health (i.e., student health and academic outcomes) will be presented to provide evidence in support of rebuilding nursing capacity in Canadian schools. Finally, the Framework for 21st Century School Nursing Practice will be introduced as a viable nursing theory to facilitate rebuilding PHN capacity in schools.

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)0.008
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.964
Threshold uncertainty score0.380

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.010
Science and technology studies0.0040.003
Scholarly communication0.0060.004
Open science0.0020.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.545
GPT teacher head0.559
Teacher spread0.014 · 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
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

Citations7
Published2021
Admission routes2
Has abstractyes

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