Transitioning: A Synergy Care Practicum for Senior Nursing Students
Bibliographic record
Abstract
References: AACN. (2010). The AACN synergy model for patient care. http://www.aacn.org/wd/certifications/content/synmodel.pcms?menu=certification;Albsmeyer, D. (2013). Individualized Capstone Experience Proposal Based on Quality and Safety Education for Nurses (QSEN) Competencies. Retrieved from http://qsen.org/individualized-capstone-experience-proposal-based-on-quality-and-safety-education-for-nurses-qsen-competencies/;Alspach, G. (2006). Extending the synergy model to preceptorship: A preliminary proposal. Critical Care Nurse, 26(2), 10-13.;Assessment Technologies Institute. (2013). ATI Product Solutions. Retrieved from https://www.atitesting.com/Solutions.asp;Bourbonnais, 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.;Charleston, R., & Happell, B. (2005). Coping with uncertainty within the preceptorship experience: the perceptions of nursing students. 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Retrieved from https://transitiontopractice.org/ toolkit.php#preceptortools.; Newhouse, R., Dearholt, S., Poe, S, Pugh, L.C., White, K. (2005). The Johns Hopkins Nursing Evidence-based Practice Rating Scale. Baltimore, MD,The Johns Hopkins Hospital; Johns Hopkins University School of Nursing. Newton, J., Cross, W., White, K., Ockerby, C., & Billett, S. (2011). Outcomes of a clinical partnership model for undergraduate nursing students. Contemporary Nurse, 39(1), 119-128. doi:10.5172/conu 2011.39.1.119.;Sullivan-Callopy, K. (1999). The synergy model in practice: Advanced practice nurses guiding families through systems. Critical Care Nurse, 19(5), 80-85.;Tuning. (2005). Approaches to teaching, learning and assessment and the subject area competencies. Retrieved from http://www.unideusto.org/tuningeu/images/stories/teaching/TLA___NURSING.pdf;Udlis, K. (2008). Preceptorship in undergraduate nursing education: an integrative review. Journal of Nursing Education, 47(1), 20-29.;Wieland, D., Altmiller, G., Dorr, M., & Wolf, Z. (2007). Clinical transition of baccalaureate nursing students during preceptored, pregraduation practicums. Nursing Education Perspectives, 28(6), 315-32.;Wolkowitz, A., & Kelley, J. (2010). Academic Predictors of Success in a Nursing Program. Journal Of Nursing Education, 49(9), 498-503. doi:10.3928/01484834-20100524-09.;Zilembo, M., & Monterosso, L. (2008). Nursing students' perceptions of desirable leadership qualities in nurse preceptors: a descriptive survey. Contemporary Nurse: A Journal For The Australian Nursing Profession, 27(2), 194-206. doi:10.5172/conu.2008.27.2.194. Acknowledgments Thanks to Dr.Sandra Lewenson, PhD for her support and enthusiasm. Dr.Kyong Hyatt, PhD, many thanks for your kind words. Dr. Shelley Barry, DNP thank you for taking me under your wing. The nursing students at MU, you are one step above the rest. My current colleagues and supervisors without you I would not be able to attend congress, thanks for adjusting the schedule. To my fiance Derek, you are the wind beneath my wings. ! Contact Information: The author can be contacted at sabrenawells@gmail.com. Table 2. The AACN Synergy Model characteristics and competencies.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.045 | 0.027 |
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".