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Record W1567857689 · doi:10.1111/jspn.12108

Integrating oral health into pediatric nursing practice: <scp>C</scp>aring for kids where they live

2015· article· en· W1567857689 on OpenAlexaff
Shelley Spurr, Jill Bally, Marcella Ogenchuk

Bibliographic record

VenueJournal for Specialists in Pediatric Nursing · 2015
Typearticle
Languageen
FieldDentistry
TopicDental Health and Care Utilization
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsReferralPediatric nursingMedicineNursingOral healthNursing practiceFamily medicineHealth care

Abstract

fetched live from OpenAlex

PURPOSE: The purpose was to identify the factors influencing pediatric oral health and describe the Caring for Kids Where They Live program. CONCLUSIONS: In North America, the burden of pediatric oral disease is significant. Despite evidence to this effect, oral health is an often-neglected aspect of pediatric nursing care. The Caring for Kids Where They Live program has successfully integrated oral health into pediatric nursing care as evidenced by increased accessibility of health care, pathways for care, and disease prevention. PRACTICE IMPLICATIONS: Pediatric nurses can address oral health disparities by integrating an oral health assessment tool as well as interprofessional follow-up and referral processes in practice.

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)0.004
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.047
GPT teacher head0.417
Teacher spread0.371 · 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.

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

Citations11
Published2015
Admission routes1
Has abstractyes

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