New Registered Nurse Transition to the Workforce and Intention to Leave: Testing a Theoretical Model
Bibliographic record
Abstract
BACKGROUND: The transition of new nurses into practice has been identified as challenging, and new nurses report having intentions to leave (ITL) jobs. Concerns of ITL are worrisome for the nursing profession, especially when faced with the need to replace an aging nursing workforce and to maintain quality patient care. PURPOSE: Guided by components of Meleis et al.'s mid-range transition theory, the purpose of this study was to test a theoretical model linking transition and ITL, as well as the personal, community and societal conditions of transition. METHODS: = 217). Structural equation modeling was undertaken to test the model. RESULTS: The new nurses reported a relatively positive transition; yet, 44% of the respondents indicated leaving their first job, and 1% departed the nursing profession. A revised model of the constructs showed a more adequate fit with the data, but overall, the hypothesized model was not supported and methodological validity of tools questioned. From the modeling, lower role stress led to a positive transition. CONCLUSIONS: Given organizational and governmental investments in orientation and transition programs, challenges in measuring transition and ITL requires additional research. Our findings highlight the value of organizations supporting new nurses by reducing role stress through reasonable workloads and expectations, which in turn contributes to a positive transition.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".