Staff nurses' experiences as preceptors and mentors: an integrative review
Bibliographic record
Abstract
AIM: The aim of this integrative review is to describe staff nurses' experience when functioning as a preceptor or mentor for student nurses. BACKGROUND: The preceptor's role is to guide students from the theory of nursing to the application of nursing theory, teaching clinical skills and clinical thinking. Relatively few research studies focus on the staff nurses' experience. EVALUATION: Research studies and topical articles from Australia, Canada, Sweden, the United Kingdom and the United States were drawn from databases. The theoretical framework for the analysis was the Kahn et al. (1964) role episode model. KEY ISSUES: Reservations over the efficacy of preceptor experiences have been identified. Along with intrinsic rewards, there is considerable stress and responsibility associated with precepting or mentoring. Nurse preceptors experience role ambiguity, conflict and overload when interacting with students. CONCLUSIONS: Research indicates what might reduce the amount of stress for the nurse preceptor and increase job satisfaction and nurse retention. IMPLICATIONS FOR NURSING MANAGEMENT: Defining and formalising the preceptor role can improve the standing of this function. Adjustments can be made to decrease the stress of the role. Preceptors and mentors request recognition and support for the amount of work involved in teaching students.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".