Bibliographic record
Abstract
BACKGROUND: If understanding our clients needs, emotions, and circumstances is fundamental to nursing practice, and empathy is the foundation of that understanding, then a conceptualization of empathy that can be used by nurses is of utmost importance to the profession. In 1992, Morse, Anderson, Bottorff, Yonge, O'Brien, Solberg and McIlveen analysed the concept of empathy in the psychological and nursing literature, and suggested the conceptualization of empathy was incomplete. Since that time, nurse authors have continued to publish conceptualizations and research on empathy. PURPOSE: The purpose of our analysis was to describe empathy as presented in the nursing literature between 1992 and 2000. METHOD: A concept clarification methodology of concept analysis was used because of the many definitions, the rich descriptions, and the application of empathy as a research variable in the reviewed literature. FINDINGS: Five conceptualizations of empathy were revealed: empathy as human trait, empathy as a professional state, empathy as a communication process, empathy as caring, and empathy as a special relationship. CONCLUSIONS: The literature reviewed contained evidence that the concept is developing more depth and breadth. Nurse authors are approaching empathy from a variety of perspectives, time frames, measurements, and outcomes. While all are important to the development of the concept, further enrichment of the conceptual work on empathy is needed before a fully mature concept emerges that is fully useful in nursing practice, research, and 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 | 0.029 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".