New Graduate Nurse Transition to Practice and Retention in Rural Settings: A Mixed Methods Study
Bibliographic record
Abstract
Given the global shortage of nurses, New Graduate Nurses (NGN) play an integral role in ensuring a strong nursing workforce. However, retention of these nurses is problematic, especially in rural settings where there remains a gap in our understanding of this concept. The purpose of this master's study is to explore the transition to practice of NGNs and the factors that influence their retention in rural settings across Ontario using a mixed methods design. Utilizing the models of Job Embeddedness (Mitchell & Lee, 2001) and Transition Stages (Boychuk Duchscher, 2008), the results show the unique challenges that influence NGNs transition to practice in these settings such as the presence of higher role expectations and responsibilities, a lack of support during orientation and the difficulties associated with working amongst casual agency nurses. By contrast, a strong sense of belonging was identified as being an important facilitator. Les infirmières nouvellement diplômées (IND) jouent un rôle essentiel. Cependant, la rétention de ces infirmières est problématique, surtout en milieu rural où il reste des lacunes dans notre compréhension de la transition de celles-ci. Le but de cette étude de maîtrise est d'explorer la transition vers la pratique des IND et les facteurs qui influencent la rétention de celles-ci dans les milieux ruraux en Ontario à l’aide d’un devis de méthodes mixtes. En utilisant les modèles d'intégration de l'emploi (Mitchell et Lee, 2001) et d'étapes de transition (Boychuk Duchscher, 2008), les résultats décrivent les défis uniques qui agissent comme barrières à la transition des IND dans ces milieux, tels que des attentes et des responsabilités plus élevées de la part des IND, le manque de soutien lors de l'orientation initiale et les difficultés associées au travail avec les infirmières d'agence occasionnelles. Des facilitateurs de transition ont également été identifiés, telle qu’un fort sentiment d’appartenance.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".