Integrating the affective domain in nursing education: A systematic review of pedagogical strategies and outcomes
Bibliographic record
Abstract
• Affective learning enhances emotional intelligence and empathy in nursing students. • This article reviews pedagogical strategies for integrating affective learning in nursing. • Findings can guide educators in implementing affective learning to improve patient care. To systematically review pedagogical strategies for integrating the affective domain in nursing education and evaluate their outcomes on emotional intelligence, professional identity, and empathy among nursing students. Systematic literature review. Peer-reviewed articles, conference papers, and academic journals from databases such as PubMed, CINAHL, and ERIC. Inclusion criteria focused on studies that implemented affective learning strategies in nursing education. Data extraction and synthesis were performed to identify common themes and outcomes. The review identified various affective learning strategies, including empathy training, reflective journaling, and immersive simulations. These strategies were found to enhance emotional intelligence, professional identity, and empathy in nursing students. Integrating the affective domain in nursing education is crucial for developing well-rounded nurses. The findings provide valuable insights for educators to implement affective learning strategies, ultimately improving patient care and professional development for associate degree nurses.
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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 | 0.021 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".