Resources Required to Develop the Nurse–Person Relationship During an Entry-to-Practice Nursing Program: A Structured Narrative Review
Bibliographic record
Abstract
Purpose: The nurse–person relationship is at the heart of the nursing discipline, nursing practice, and nursing education. While various aspects of this phenomenon are broached in different articles, none of these works focus on teaching the development of such a comprehensive relationship during an entry-to-practice nursing program, which has the effect of masking its complex nature. Guided by the competency-based approach described by Tardif (2016), this structured narrative review seeks to identify the resources acquired during an entry-to-practice a nursing program for developing and upholding the nurse–person relationship. Method: With help from a librarian specialized in health sciences, a search of the literature was conducted in CINAHL, MEDLINE, and Web of Sciences. A total of 1,163 articles were found, and 18 recent articles (2010–2023) were selected. A thematic analysis was then performed by using Tardif’s competency-based approach framework (2006). Results: The following two themes were identified: a) internal resources that students can incorporate, among them empathy, compassion, communication, and true respect; b) external resources that are found in students’ environments and that support them, including confidence and role models. Conclusion: Without presuming to have revealed all the resources necessary for nurse–person relationships, this structured narrative review allows for synthesizing existing knowledge of the resources most often cited in scientific works. Persons in charge of an entry-to-practice nursing program will be able to use the results of this structured narrative review to adapt their teachings. These results will also likely prove useful to research teams seeking avenues for future research on this topic.
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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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".