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Record W2093758510 · doi:10.1515/ijnes-2012-0008

Voices of Innovation: Building a Model for Curriculum Transformation

2013· article· en· W2093758510 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 · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Learning Practices
Canadian institutionsYork University
Fundersnot available
KeywordsCurriculumTransformation (genetics)Public healthSociologyEngineeringComputer scienceMedicinePedagogyNursing

Abstract

fetched live from OpenAlex

Innovation in nursing education curriculum is critically needed to meet the demands of nursing leadership and practice while facing the complexities of today's health care environment. International nursing organizations, the Institute of Medicine, and; our health care practice partners have called for curriculum reform to ensure the quality and safety of patient care. While innovation is occurring in schools of nursing, little is being researched or disseminated. The purposes of this qualitative study were to (a) describe what innovative curricula were being implemented, (b) identify challenges faced by the faculty, and (c) explore how the curricula were evaluated. Interviews were conducted with 15 exemplar schools from a variety of nursing programs throughout the United States. Exemplar innovative curricula were identified, and a model for approaching innovation was developed based on the findings related to conceptualizing, designing, delivering, evaluating, and supporting the curriculum. The results suggest implications for nursing education, research, and 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.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.004
Open science0.0010.000
Research integrity0.0000.000
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 head0.100
GPT teacher head0.491
Teacher spread0.391 · 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