Understanding How Nursing Students Experience Becoming Relational Practitioners: A Narrative Inquiry
Bibliographic record
Abstract
BACKGROUND: Teaching nursing students to become relational practitioners requires theoretical approaches and strategies that engender personal and aesthetic knowing. These qualities closely parallel those that define relational practice. The use of creative self-expression in supporting the development of student capacity for relational practice offers a viable approach. PURPOSE: To learn how nursing students' engagement in creative self-expression activities may impact the construction of their professional identity and capacity for relational practice as novice nurses. METHOD: Clandinin and Connelly's narrative inquiry approach was used to explore nursing students' experiences of learning how to become relational practitioners. Four new nurse graduates engaged in a follow-up focus group using Schwind's narrative reflective process to discuss the impact of a relational practice workshop series. FINDINGS: These entailed an intentional engagement in relationships with patients, which required attention to the co-constructed relational space. The creative approaches used to facilitate students' learning informed their awareness that led to their transformation. IMPLICATIONS: Educating future nurses who are relational, person-centered practitioners requires a holistic approach to teaching/learning which also includes creative self-expression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".