The Narrative Circle Model: An interpretative framework for nursing education and research
Bibliographic record
Abstract
BACKGROUND: A research study entitled "Newly-Graduated Baccalaureate Registered Nurses, the 21st Century Health Care Environment and Mapping the Landscape for Curricular Change" explored the perceptions of newly graduated registered nurses (NGRNs). During the research process, a model for nursing education and research was revealed and subsequently developed as an interpretative framework. PURPOSE: Qualitative narrative inquiry research explored the perceptions of newly graduated registered nurses and shaped the creation of the Narrative Circle Model for Nursing Education and Research (NCMNER). This paper will explain how the model represents the cyclic yet reciprocal relationship among education, research, and practice using narratives. DESCRIPTION: The NCMNER provides a unique perspective by illustrating knowledge gained from narratives of NGRNs using narrative inquiry research methodology to influence nursing education and practice. Ultimately, the model will illustrate the significant implications of education and research in advancing the future of nursing with educational, social, and political change. CONCLUSION: Concepts from the NCMNER provided an interpretative framework for the major findings of the research; specifically, narratives used in educational curriculum, narratives from research methodology, and hence the relationships between narratives in education and research.
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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 | 0.067 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.008 | 0.041 |
| Scholarly communication | 0.019 | 0.024 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".