Voices of Innovation: Building a Model for Curriculum Transformation
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.037 |
| Scholarly communication | 0.020 | 0.032 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".