Bibliographic record
Abstract
Guided by the humanbecoming leading-following model, the author designed a nursing theories course with the intention of creating a meaningful nursing theory to practice link. The author perceived that with the implementation of Situation-Background-Assessment-Recommendations (SBAR) communication, nursing staff had drifted away from using the Kardex™ in shift to shift reporting. Nurse students, faculty, and staff members supported the creation of a theories project which would engage nursing students in the pursuit of clinical excellence. The project chosen was to revise the existing Kardex™ (predominant nursing communication tool). In the project, guided by a nursing theory, nursing students focused on the unique patient's experience, depicting the specific role of nursing knowledge and the contributions of the registered nurse to the patient's healthcare journey. The emphasis of this theoretical learning was the application of a nursing theory to real-life clinical challenges with communication of relevant, timely, and accurate patient information, recognizing that real problems are often complex and require multi-perspective approaches. This project created learning opportunities where a nursing theory would be chosen by the nursing student clinical group and applied in their clinical specialty area. This practice activity served to broaden student understandings of the role of nursing knowledge and nursing theories in their professional practice.
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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 | 0.042 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".