Mentoring non-traditional students in clinical practicums: Building on strengths
Bibliographic record
Abstract
Background: As nurse educators respond to increasing numbers of adult learners attending practicum experiences, clinical instructors are one of our richest resources. And yet, the everyday strategies they implement to mentor these non-traditional students towards success may go unnoticed. This article describes findings from a qualitative descriptive research study that listened to the voices of experienced clinical instructors.Objective: The objective of the study was to describe effective mentoring approaches that instructors in a Post Licensed Practical Nurse to Bachelor of Nursing (Post LPN to BN) program used to support students’ learning and build on their strengths during instructor led clinical practicum courses.Methods: The research was framed from a constructivist worldview and Laurent Daloz’s mentoring model. Digitally recorded and transcribed interview data was collected from 10 clinical instructors who had been teaching for more than 5 years. The transcripts were analyzed for themes which were confirmed with participants through member checking.Results: Findings revealed that instructors supported students by validating individual strengths; challenged them by building on those strengths; and created vision by linking their present activities to competencies needed in their own future practice.Conclusions: These findings provide valuable insights and guidance to practicing Registered Nurses (RN’s) interested in teaching non-traditional students during their clinical experiences.
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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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".