Experiences of Preceptors in Dedicated Education Units in the Public Hospital Environment
Bibliographic record
Abstract
• Bourbonnais, F. F., & Kerr, E. (2007). Preceptoring a student in the final clinical placement: Reflections from nurses in a Canadian hospital. Journal of Clinical Nursing, 16(8), 1543-1549. • Brammer, J. (2006). A phenomenographic study of registered nurses’ understanding of their role in student learning: An Australian perspective. International Journal of Nursing Studies, 43(8), 967-973. • Casey, M., Hale, J., Jamieson, I., Sims, D., Whittle, R., & Kilkenny, T. (2008). Kai Tiaki. Nursing New Zeland(11). • Gonda, J., Wotton, K., & Mason, P. (1999). Dedicated Education Units: 2 An evaluation Contemporary Nurse, 8, 172-176. • Kaviani, N., & Stillwell, Y. (2000). An evaluative study of clinical preceptorship. Nurse Education Today, 20(3), 218-226. • Lillibridge, J. (2007). Using clinical nurses as preceptors to teach leadership and management to senior nursing students: A qualitative descriptive study. Nurse Education in Practice, 7(1), 44-52. • Miller, T. (2005). The Dedicated Education Unit. Nursing Leadership Forum, 9(4), 169-173. • Ohrling, K., & Hallberg, I. (2001). The meaning of preceptorship: Nurses’ lived experience of being a preceptor. Journal of Advanced Nursing, 33(4), 530-540. • Pappas, S. (2007). Improving patient safety and nurse engagement with a Dedicated Education Unit. Nurse Leader(6), 40-43. • Ranse, K., & Grealish, L. (2007). Nursing students’ perceptions of learning in the clinical setting of the Dedicated Education Unit. Journal of Advanced Nursing, 58(2), 171-179. • Stevenson, B., Doorley J., Moddeman, G., & Benson-Landau, M. (1995). The preceptor experience: A qualitative study of perceptions of nurse preceptors regarding the preceptor role. Journal of Nursing Staff Development, 11(3), 160-165. • Wotton, K., & Gonda, J. (2004). Clinician and student evaluation of a collaborative clinical teaching model. Nurse Education in Practice, 4, 120-127. CONCLUSIONS
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".