Exploring impacts of student-nurse relationships: Views from across the curriculum
Bibliographic record
Abstract
Background and objectives: The clinical encounters that student nurses experience during their training make an impressionable impact on their learning. Previous studies have examined the perceptions of staff nurses in serving as preceptors or mentors, but there is limited literature focused on the student experience. This study aims to highlight the student perspective on working with nurse mentors during various levels of their undergraduate nursing program.Methods: This study applied a qualitative descriptive design. Purposive sampling was conducted among undergraduate nursing students attending a four-year Midwest university. A total of 19 baccalaureate students were interviewed using conversational-style interviews. This included three different focus group sessions; one designated for each level of the program. Nvivo professional services provided verbatim transcription. The data management was supported by Google Docs. The data was analyzed using qualitative thematic analysis.Results: Four common themes emerged among the focus groups related to the impact of interactions with nurse mentors on student learning; the themes were (1) sense of belonging, (2) helpful staff approaches, (3) burden, and (4) experience with clinical instructor.Conclusions: The results from this study add important insight into the student perspective on working with staff nurse mentors during their clinical experience. The findings underscore the critical impact these relationships have on student learning throughout their nursing 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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".