From nursing students to newly licensed registered nurses: A longitudinal survey study on burnout, perceived stress, generalized health, and intent to leave
Bibliographic record
Abstract
Introduction: There is a critical nursing shortage worldwide, and newly licensed registered nurses (NLRN) are needed to fill the gap. There is a consistent loss of 1/3 NLRN in the first year and 1/5 within the second-year post-graduation. This loss has been attributed to burnout, with NLRN leaving the workforce (job) or nursing field. The aim of this longitudinal survey study was to examine the potential relationship between burnout, perceived stress, and generalized health with intent to leave the workforce. Methods: Surveys were administered from September 2023 to December 2024: baseline, graduation, 3-month, and 6-month post-graduation. Measures used were Maslach Burnout Inventory, Perceived Stress Scale, Generalized Health Questionnaire-12, and intent to leave. Results: Emotional exhaustion had a very strong direct correlation with depersonalization (.778) indicating the higher level of emotional exhaustion, the higher degree of depersonalization. Emotional exhaustion had a very strong direct correlation with perceived stress (.875), indicating the higher the perceived stress, the higher the degree of emotional exhaustion. Bachelor degree students showed higher levels of emotional exhaustion compared to associate degree students. Depersonalization had a moderately-strong correlation with thoughts of entering nursing, although 55% disagree about thoughts of leaving the nursing profession. Conclusions: This study lays a crucial longitudinal foundation on burnout, perceived stress, generalized health, degree program, and intent to leave. This study showed a very strong correlation between emotional exhaustion-perceived stress, emotional exhaustion-depersonalization, with students in Bachelor degree programs showing higher levels of emotional exhaustion. However, more research is needed exploring degree program, burnout, perceived stress, and their impact on intent to leave.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".