The meaning of caring for nursing students in a baccalaureate nursing program: An arts-based inquiry
Bibliographic record
Abstract
Objective: To determine the meaning of caring for nursing students in order to inform development of a caring curriculum for a four-year Bachelor of Nursing Program.Methods: A hermeneutic phenomenological method was employed to explore the meaning students ascribed to caring in nursing. Students drew from their own experiences within the context of nursing education. Arts-based inquiry was used as the medium to elicit students’ reflections of the meaning of caring. Seven nursing students participated in the study. Each student was asked to paint a picture capturing the meaning of caring in nursing, followed by one semi-structured audio-recorded interview. Data analysis followed the seven-step method of contextual analysis described by Diekelmann, Allan and Tanner (1989), and incorportated the methods of van Manen (1990).Results: Four themes emerged from the interview data: a) caring comes from within, b) caring is being the best you can be, c) caring is providing holistic care, and d) caring cannot be taught.Conclusions: Arts-based inquiry and the phenomenological method enabled in-depth exploration of the meaning of caring in nursing for seven nursing students. Arts-based inquiry can serve as an effective educational strategy for facilitating and fostering nurse caring among nursing students. The findings from this study have important implications for designing and implementing a caring curriculum in a baccalaureate nursing program including ensuring a caring learning environment is established for nursing students. A caring curriculum will advance student caring, and, ultimately, promote higher quality nursing care delivery.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".