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Record W2053620262 · doi:10.3928/00220124-20080401-02

Helping Faculty Enhance Scholarship

2008· review· en· W2053620262 on OpenAlexaff
Pamela Hawranik, Karran Thorpe

Bibliographic record

VenueThe Journal of Continuing Education in Nursing · 2008
Typereview
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsScholarshipEconomic shortageCurriculumCompetence (human resources)Medical educationNurse educatorNurse educationNursingSociologyMedicinePsychologyPedagogyEngineering ethicsPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Nurse educators face a myriad of challenges (e.g., changing student populations, increased demand for the use of technology, faculty shortages, and facilitating the development of self-confidence and competence in students) as they endeavor to enhance scholarship and quality in nursing education. Scholarship encompasses four separate but integrated elements (i.e., discovery, integration, application, and teaching) that need to be instilled in nursing students to prepare them for diverse roles in the profession of nursing. Implications for nurse educators relate to creating curricula that support scholarship, technological and interprofessional opportunities, and strategies for socializing students into scholarship.

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)0.006
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0020.003
Scholarly communication0.0050.007
Open science0.0010.008
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0100.005

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 head0.042
GPT teacher head0.461
Teacher spread0.418 · 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.

Study designNot applicable
DomainMethods
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

Citations16
Published2008
Admission routes1
Has abstractyes

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