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Record W4280604365 · doi:10.1515/ijnes-2021-0091

Pediatric practicums in undergraduate nursing programs: an integrative review

2022· review· en· W4280604365 on OpenAlexaff
Oghenerukevwe Onororemu, Jonathan Alschech, Janet McCabe, Caroline Sanders

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2022
Typereview
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsOntario Tech UniversityUniversity of Northern British Columbia
Fundersnot available
KeywordsPracticumThematic analysisMedical educationNursingInclusion (mineral)Nurse educationVariety (cybernetics)Promotion (chess)MedicinePsychologyQualitative researchSociologyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Practicum Placements are the signature andragogy used in undergraduate nursing programs to bring about knowledge consolidation. Little is known, however, about the types of pediatric practicum placements utilized by nursing programs to provide practical learning opportunities in child health to their students. The purpose of this integrative review is to identify and appraise existing literature on practical pediatric practices in undergraduate nursing education. METHOD: Searches were conducted using the main relevant databases and search engines. Of the numerous articles retrieved, screened, and reviewed, 15 met the inclusion and exclusion criteria. Results were then analyzed using thematic analysis. RESULTS: Two overarching themes were identified: (1) Adaptation, and (2) Learning. CONCLUSION: Findings revealed that most nursing programs utilize alternative placement sites to meet the learning need of their students. These sites provided students with the opportunities to care for children and adolescents, and engage in a variety of health promotion and teaching activities.

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)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0100.011
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.153
GPT teacher head0.506
Teacher spread0.353 · 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 designSystematic review
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

Citations2
Published2022
Admission routes1
Has abstractyes

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