Bibliographic record
Abstract
BACKGROUND: Although central to educational decision-making, value in nursing education is rarely defined. As programs evolve through curricular reform, competency-based design, simulation, artificial intelligence, and digital learning, what makes these efforts genuinely valuable remains unclear. PURPOSE: This paper introduces the REFLECT Framework, a reflective conceptual model for guiding value-based decisions in nursing education. METHODS: Using an integrative analysis of literature from healthcare, education, and implementation science, value is conceptualized as a multidimensional construct encompassing seven dimensions: purpose alignment, evidence-informed pedagogy, pedagogical expertise, learner-centeredness, resource stewardship, equity, and contextual fit and sustainability. DISCUSSION: These dimensions support reflection on whether educational initiatives are effective, ethical, equitable, feasible, and aligned with nursing's professional and societal purposes. The framework can be applied at micro (course), meso (program), and macro (system) levels to guide deliberation about what matters, for whom, and under what conditions. CONCLUSION: The REFLECT Framework offers a structured, context-sensitive approach for advancing a deliberate and sustainable approach to shaping nursing education.
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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 | 0.025 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.055 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".