Proposing a policy framework for nursing education for fostering compassion in nursing students: A critical review
Bibliographic record
Abstract
AIMS: To propose a policy framework for nursing education to foster compassion in nursing students. DESIGN: A critical review. DATA SOURCES: Literature was searched in CINAHL, PubMed, Science Direct and Google Scholar and sources published from January 2008 - April 2018 were reviewed. REVIEW METHODS: We screened abstracts and full-texts using specific inclusion criteria, developed summary tables for data extraction and synthesized data logically to develop the framework. RESULTS: Twenty-nine sources were reviewed. Recognizing, accepting, and alleviating patients' suffering are direct indicators of compassionate care. Three policy directions were identified: ensure the nursing curriculum has an appropriate balance of teaching-learning strategies that target learning in the affective domain, directly promote the use of reflection and the development of reflective thinking in students as an approach to enhance excellence in clinical practice and integrate information and assess students' understanding and expression of compassion throughout the nursing curriculum. CONCLUSION: Compassion is expressed when nurses authentically work to understand patients' suffering and become sensitive to their experiences. Future research should focus on developing strategies that align with the affective domain and use reflection to optimize nursing students' experiential learning. IMPACT: Policies are needed for cultivating a compassionate care culture and for fostering students' compassion, but no guidelines exist for nursing institutions. Targeting the affective learning domain, facilitating reflection, and integrating compassionate care indicators in clinical learning experiences can be useful. Therefore, nursing institutions can use these findings to integrate and measure compassionate care in clinical and educational curricula to foster students' compassion.
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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 | 0.361 | 0.387 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.028 | 0.014 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.022 | 0.031 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.020 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".