Nursing Students and Nurses’ Recommendations Aiming at Improving the Development of the Humanistic Caring Competency
Bibliographic record
Abstract
BACKGROUND: Most nursing education programs prepare their students to embody humanism and caring as it is expected by several regulatory bodies. Ensuring this embodiment in students and nurses remains a challenge because there is a lack of evidence about its progressive development through education and practice. PURPOSE: This manuscript provides a description of nursing students' and nurses' recommendations that can foster the development of humanistic caring. METHODS: Interpretive phenomenology was selected as the study's methodological approach. Participants (n = 26) were recruited from a French-Canadian university and an affiliated university hospital. Data was collected through individual interviews. Data analysis consisted of an adaptation of Benner's (1994) phenomenological principles that resulted in a five-stage interpretative process. RESULTS: The following five themes emerged from the phenomenological analysis of participants' recommendations: 1) pedagogical strategies, 2) educators' approach, 3) considerations in teaching humanistic caring, 4) work overload, and 5) volunteerism and externship. CONCLUSION: The findings suggest the existence of a challenge when using mannikins in high-fidelity simulations with the intention of developing humanistic caring. The findings also reaffirm the importance of giving concrete and realistic exemplars of humanistic caring to students in order to prevent them from making "communication" synonymous to "humanization of care".
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".