Teaching students to teach patients: A theory-guided approach
Bibliographic record
Abstract
Nurses in every setting provide patient teaching on a routine basis, often several times a day. Patient teaching skills are essential competencies to be developed during pre-licensure nursing education. While students learn what to teach for specific conditions, they often lack competence in how to teach in ways that individualize and optimize patient learning. The ultimate goal of patient teaching is to arm patients with the knowledge and skills, and the desire and confidence in their ability to reach their targeted health outcomes. We describe the creation of a theoretical framework to guide development of patient teaching skills. The framework, rooted in the contemporary health care values of patient-centered care, is a synthesis of four evidence-based approaches to patient teaching: patient engagement, motivational interviewing, adult learning theory, and teach-back method. Specific patient teaching skills, derived from each of the approaches, are applied within the context of discharge teaching, an important nursing practice linked to patient outcomes. This exemplar emphasizes the use of critical teaching process skills and targeted informational content. An online student learning module based on the theoretical framework and combined with simulation experiences provides the nurse educator with one strategy for use with nursing students. The theoretical framework has applicability for skill development during pre-licensure education and skill refinement for nurses in clinical 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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".