Retaining and Transferring Nursing Knowledge through a Hospital Internship Program
Bibliographic record
Abstract
In healthcare, organizations recognize that human capital is their most valuable asset. The importance of investing in knowledge workers is imperative given the current and future shortage of health professionals. Knowledge acquisition occurs through continuous learning and the transfer of information from those who are highly experienced to those who are less qualified. At St. Michael's Hospital, two innovative and unique programs were created for the transfer of knowledge. The first was a nurse fellowship program that enabled experienced nurses to spend two to three months learning new skills to advance clinical practice. The second was a nurse internship program in which new graduates spend three to four months in their area of hire to enhance clinical practice through skill development and prioritization of patient care needs. This paper describes both programs and presents an evaluation of the new-graduate internship program as an opportunity for professional development and career enhancement For nurse interns, the program promotes self-esteem and professional confidence, improves job satisfaction and rewards nurses for their contribution. For nurse preceptors, the program provides job enrichment, experience in teaching and recognition by the organization and peers that they are knowledge experts. In healthcare, organizations have come to acknowledge that their most valuable asset is human capital, in particular, knowledge workers (Horibe 1999). Knowledge workers contribute a composite of information, intellectual property and experience (Horibe 1999), acquired by study, investigation, observation or practice (Webster's Dictionary 1989). Investing in knowledge workers is investing in the future. In this regard, organizations have implemented recruitment and retention strategies to attract, retain and advance the highest calibre of health professional. Knowledge workers contribute to an organization through their ideas, analyses of complex situations and sound judgment in decision-making (Horibe 1999). They further develop these skills over the course of their career through continuous learning. This paper will focus on the importance of investing in nurses as knowledge workers. In particular, given the shortage of nurses and the reality that 25% of today's nurses can retire over the next 10 years (CNA 2001), it is imperative that knowledge transfer occur from highly experienced to less experienced nurses.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".