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Record W2907642729 · doi:10.1097/nne.0000000000000642

Implementing Universal Design Instruction in Doctor of Nursing Practice Education

2018· article· en· W2907642729 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

VenueNurse Educator · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsBC Studies
Fundersnot available
KeywordsCurriculumModalitiesMedical educationProcess (computing)Nurse educatorWork (physics)Nurse educationCurriculum developmentDoctor of Nursing PracticeMedicinePedagogyComputer sciencePsychologySociologyEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: Faculty in Doctor of Nursing Practice (DNP) programs identify challenges of increased enrollment and variances in previous student educational preparation and professional experiences that require innovative approaches to curriculum transformation. PURPOSE: This article informs nurse educators about the vibrant and inclusive approach of universal design for instruction (UDI), a framework to conceptualize and implement learning strategies in the DNP curriculum. APPROACH: UDI is guided by 9 instructional principles that anticipate diverse learners and is intentionally inclusive of multiple ways of learning. Principles of UDI were synergized into several DNP didactic courses and the scholarly project process. CONCLUSION: Integration of UDI into the DNP curriculum included precourse assessments, multiple modalities of content delivery, options to present acquired knowledge, and supporting the adult learner through the iterative work of the scholarly project.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.059
GPT teacher head0.440
Teacher spread0.381 · 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