New Graduate RNs' Perceptions of Transitioning to Professional Practice After Completing Ontario's New Graduate Guarantee Orientation Program
Bibliographic record
Abstract
BACKGROUND: By 2022, Canada will be short 60,000 RNs. Contributing to this shortage are difficulties experienced by new graduate RNs (NGRNs) transitioning to professional practice. METHOD: This grounded theory study explored NGRNs' transition experiences in the 12 months after completing Ontario's New Graduate Guarantee orientation program. Semistructured interviews were conducted with 10 NGRNs on the Nursing Resource Team in one urban Ontario academic hospital network. RESULTS: Discovering Professional Self described NGRNs' transition as progressive, with transitory setbacks. In the early part of the transition, NGRNs experienced Surviving Without a Safety Net, which involved Experiencing Fear, Figuring It Out, and Learning on the Job. In the later part of transition, the NGRNs experienced Turning of the Tables, which involved Being Trusted, Gaining Confidence, and Feeling Comfortable in their professional role. CONCLUSION: Recommendations focus on educational strategies to enhance the NGRNs' transition experience.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".