Factors Associated With Work Engagement of Nurses During the Fifth Wave of the COVID-19 Pandemic in Japan: Web-Based Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has brought to light the prevalence of mental health issues among nurses. Work engagement (WE) is a concept that describes work-related positive psychological states and is of importance within mental health measures. There is, however, a lack of research on factors associated with the WE of nurses during the COVID-19 pandemic. OBJECTIVE: We aimed to determine which factors are associated with WE among nurses during the COVID-19 pandemic using the job demands-resources (JD-R) model as a framework. METHODS: A web-based cross-sectional survey was conducted among nurses working in acute care and psychiatric institutions in the prefectures of Chiba and Tokyo in Japan. The survey period occurred between August 8 and September 30, 2021, during a time when the number of patients with a positive COVID-19 infection increased. The 3-item version of the Utrecht Work Engagement Scale (UWES-3) was used to measure WE. Factors such as age, gender, years of experience, affiliated ward, COVID-19-related stress, financial rewards from the government and hospital, encouragement from the government and patients, and workplace social capital were assessed. A total of 187 participants were included in the final analysis. Multiple regression analysis was performed to examine the factors related to WE. Partial regression coefficients (B), 95% CI, and P values were calculated. RESULTS: The mean overall score for the UWES-3 was 3.19 (SD 1.21). Factors negatively associated with UWES-3 were COVID-19-related stress on work motivation and escape behavior (Β -0.16, 95% CI -0.24 to -0.090; P<.001), and factors positively associated with UWES-3 were affiliation of intensive care units (Β 0.76, 95% CI 0.020-1.50; P=.045) and financial rewards from the government and hospital (Β 0.40, 95% CI 0.040-0.76; P=.03). CONCLUSIONS: This study examined factors related to WE among nurses during the COVID-19 pandemic using the JD-R model. When compared with findings from previous studies, our results suggest that nurses' WE was lower than before the COVID-19 pandemic. Negative motivation and escape behaviors related to COVID-19 were negatively associated with WE, while there were positive associations with financial rewards from the government and hospital and affiliation with an intensive care unit. Further research into larger populations is needed to confirm these findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".