The One-to-One Relationship: Is it Really Key to an Effective Preceptorship Experience? A Review of the Literature
Bibliographic record
Abstract
Currently, considerable focus is directed at improving clinical experiences for nursing students, with emphasis placed on adequate support and supervision for the purpose of creating competent and safe beginning practitioners. Preceptors play a vital role in supporting, teaching, supervising and assessing students in clinical settings as they transition to the graduate nurse role. Intrinsic to this model is the assumption that the one-to-one relationship provides the most effective mechanism for learning. With the current Registered Nurses (RN) shortage, among other factors, the one-to-one relationship may not be feasible or as advantageous to the student. Thus, nurse educators need to carefully assess how this relationship is configured and maintained to assist them in fostering its evolution. In this review of the literature, the authors explore the assumption that a one-to-one relationship in the preceptorship experience fosters a rich and successful learning environment, and implications for nursing education, practice and research are outlined.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".