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Record W3035662703 · doi:10.1515/ijnes-2019-0047

A Comparative Analysis of Teaching and Evaluation Methods in Nurse Practitioner Education Programs in Australia, Canada, Finland, Norway, the Netherlands and USA

2020· article· en· W3035662703 on OpenAlexaffabout
Nicole Jeffery, Faith Donald, Ruth Martin‐Misener, Denise Bryant‐Lukosius, Edda Johansen, H. Ösp Egilsdottir, Judy Honig, Haakan Strand, Krista Jokiniemi, N Carter, Pieternella Roodbol, Sarah Rietkoetter

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2020
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsMcMaster UniversityDalhousie UniversityToronto Metropolitan University
Fundersnot available
KeywordsNurse practitionersNursingContent analysisNurse educationMedicineNurse educatorCurriculumMedical educationPsychologyHealth carePedagogySociologyPolitical science

Abstract

fetched live from OpenAlex

A scoping review of published literature and dialogue with international nurse practitioner educators and researchers revealed the education of nurse practitioner students varied within and between countries. This lack of cohesiveness hinders nurse practitioner role development and practice nationally and internationally. A rapid review of grey literature was conducted on nurse practitioner education standards in six countries (Australia, Canada, Finland, Norway, the Netherlands, and USA). Data were extracted from graduate level nurse practitioner education programs' websites from each country (n = 24). Extracted data were verified for accuracy and completeness with a nurse practitioner educator from each program. Data were analyzed using content analysis. Variations in nurse practitioner education within and between countries were explored by comparing admission criteria, curricular content, clinical requirements, teaching methods, and assignment and evaluative methods. The findings will help inform education programs and further research about nurse practitioner education internationally.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.301
GPT teacher head0.603
Teacher spread0.303 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations17
Published2020
Admission routes2
Has abstractyes

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