EDUCATIONAL FACTORS INFLUENCING THE SELF-EFFICACY OF NEW GRADUATE NURSES FOR PROFESSIONAL COMPETENCIES DURING THE TRANSITION TO REGISTERED NURSE
Bibliographic record
Abstract
Problem: New graduate nurses (NGNs) are often considered the solution to the global\nnursing shortage. However, researchers are reporting an alarming new trend; NGNs are leaving the profession (Duchscher & Cowin, 2004; Sochalski, 2002). They are unprepared to work in the ‘real world’, even after successfully graduating from a nursing program.\nAim: This study examined the relationship between each of four educational factors (biological science courses, clinical practicum in undergraduate education; orientation/training provided by the employing hospital; post-registration preceptorship) and NGNs’ self-efficacy (confidence) for professional competencies as they transition to the role o f registered nurse.\nMethods: Based on Bandura’s theory of self-efficacy, a researcher-designed self-report questionnaire was mailed to 339 NGNs working in acute care hospitals across the province of Ontario. Results: Post-registration preceptorship most positively influenced NGNs’ confidence with their professional competencies. Specifically, preceptorship lasting at least 4 weeks, and the consistency of one preceptor, contributed to greater confidence for NGNs
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".