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Record W2333813531 · doi:10.2202/1548-923x.2234

Teaching Statistics to Undergraduate Nursing Students: An Integrative Review to Inform our Pedagogy

2011· article· en· W2333813531 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2011
Typearticle
Languageen
FieldMathematics
TopicStatistics Education and Methodologies
Canadian institutionsYork UniversityToronto Metropolitan UniversityGeorge Brown College
Fundersnot available
KeywordsMedical educationNurse educationQuality assurancePsychologyNursing researchQuality (philosophy)MedicineNursing

Abstract

fetched live from OpenAlex

One goal of undergraduate nursing education is to develop competency in statistics interpretation. This competency requires adequate knowledge and skill to read and analyze the merits of research studies, quality assurance data, and patient data. The literature suggests that RNs may lack undergraduate, entry-to-practice competencies regarding statistical analysis. This review explores and critically appraises the international nursing literature concerning the pedagogy of teaching undergraduate statistics to nursing students. The following dominant three themes: (1) student and faculty characteristics, (2) conceptual framework and (3) the course characteristics of content and delivery guided our review. It was found that there is limited to no evidence concerning the pedagogy of statistics; further research is needed to establish best practices based on evidence.

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.

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)0.003
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.698
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.405
GPT teacher head0.618
Teacher spread0.213 · 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