Nurse Preceptors’ Experiences of an Online Strength-Based Nursing Course in Clinical Teaching
Bibliographic record
Abstract
Background: Online educational programs for nurse preceptors have been created based on various theoretical frameworks; however, no programs using a Strengths-Based Nursing (SBN) approach could be located. Purpose: This qualitative descriptive study explored the nurse preceptors’ experiences in using a SBN approach to provide clinical teaching to nursing students after completing an online SBN clinical teaching course. Methods: Semi-structured interviews were conducted with six nurses. Data was thematically analyzed. Findings: Although their levels of familiarity with SBN varied, all preceptors acknowledged that using a SBN approach in clinical teaching benefits both students and educators. They reported that it empowered students and that it allowed them to discover their strengths. Getting to know their students helped the preceptors provide tailored learning experiences and feedback. Using the SBN approach simultaneously enhanced the preceptors’ self-confidence and created opportunities for shared learning. Conclusion: Using a strengths’ approach offers nurse preceptors a powerful tool to facilitate student learning and skills development in clinical practice.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".