Impact of the Work Environment on Nurse Outcomes: A Mediation Analysis
Bibliographic record
Abstract
Background:The nursing workforce remains in a vulnerable state post pandemic as working conditions are difficult and exacerbated by a global nursing shortage. Identifying factors leading to turnover intentions are thus critical for health care system recovery.Purpose:The purpose of this study was to examine the impact of nurses’ work environment and the pandemic on missed nursing care, scope of practice, emotional exhaustion, and intent to leave.Methods:This study was a cross-sectional, self-reporting online survey, sent to hospital-based nurses in a Canadian province (n = 419). Mediation analysis was used to examine both direct and indirect effects of work environment and COVID-19 impact on nurse outcomes (emotional exhaustion and intent to leave) through missed care and scope of practice.Results:The results showed that 73% of nurses were considering leaving the profession. Several direct and indirect pathways predicted emotional exhaustion and intent to leave. A better work environment was related to both decreased emotional exhaustion and intent to leave. Nurses’ scope of practice partially mediated the relationship between work environment and intent to leave. On the other hand, missed care did not mediate emotional exhaustion or intent to leave.Conclusions:While considering the global nursing shortage, it is imperative to implement strategies to promote nurses’ well-being and their retention within the health care system.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".