Preceptor and learner perspectives on LPN-BN transition to practice: A pilot study
Bibliographic record
Abstract
Background and purpose: The regulated nursing profession in Canada includes Licensed Practical Nurses (LPNs) with diploma qualifications and Registered Nurses (RNs) with baccalaureate degrees. Nursing programs, such as the LPN to Bachelor of Nursing (BN) post-program, prepare LPNs for RN qualifications after successful licensure. RN preceptors mentor these learners for the RN role during the final consolidation practicum (CP). This pilot study examined what constitutes a successful transition for LPN to BN learners and explored key markers of this transition from the perspectives of both learners and preceptors. The study also provides insights into factors influencing the transitional experiences of learners during their final CP.Methods: A qualitative interpretive descriptive approach with a two-phase research design was employed. In phase one, preceptors (n = 12) and learners (n = 10) completed an online survey comprised of open-ended and closed-ended questions. Phase two included semi-structured interviews with survey participants. De-identified survey data and interview transcripts were analysed using Braun and Clarke's thematic analysis.Results: Participants identified markers of role transition, including altered thought processes, increased confidence in the new role, and the development of skills and competencies. Both positive and negative influences on the transition process were also reported.Conclusions: The insights gained from this pilot study can inform improvements in nursing education, particularly in LPN to BN post programs. Understanding the factors affecting transition can guide curricula development to enhance the successful transition of LPNs to RNs, thus empowering educators and policymakers to make effective changes in 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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".