“How Can We Do Better?”: A Case Study of a Pre-Implementation Analysis of a Residency Program for New Graduate Nurses in Canada
Bibliographic record
Abstract
Background Up to 33% of newly graduated nurses leave the profession within the first two years. This high turnover rate can burden care teams, negatively impacting the quality of care provided. To alleviate this problem, transition programs are offered to new nurses; however, they vary considerably in type and duration. Despite this heterogeneity, many researchers conclude that transition programs have a positive overall effect on new nurses’ competencies, self-confidence, satisfaction, stress, and retention, especially when they are longer than six months and have an explicit framework and structure, such as residency programs. Purpose To conduct a pre-implementation analysis of a residency program in the Canadian context. Methods Using a case study methodology, two sequential steps were performed to model the already implemented transition program and its components that needed to be upgraded to a residency program. Data were collected through 1) document analysis ( n = 1,601) with selected interviews of stakeholders ( n = 5) and 2) a survey with new graduate nurses ( n = 29) and preceptors ( n = 11). Results A preliminary logic model of the program was developed, depicting the structure of the proposed activities in terms of organizational orientation, unit integration, autonomous practice, and additional support measures. The operationalization of some program components was variable and sometimes missing, thereby affecting its quality. Conclusion This study showed how transition programs already implemented in clinical settings can be enhanced into residency programs by conducting a pre-implementation analysis. This can positively impact the transition of newly graduated nurses, including their retention.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".