Effect of mode of healthcare delivery on job satisfaction and intention to quit among nurses in Canada during the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic resulted in a major shift in the delivery of healthcare services with the adoption of care modalities to address the diverse needs of patients. Besides, nurses, the largest profession in the healthcare sector, were imposed with challenges caused by the pandemic that influenced their intention to leave their profession. The aim of the study was to examine the influence of mode of healthcare delivery on nurses' intention to quit job due to lack of satisfaction during the pandemic in Canada. This cross-sectional study utilized data from the Health Care Workers' Experiences During the Pandemic (SHCWEP) survey, conducted by Statistics Canada, that targeted healthcare workers aged 18 and over who resided in the ten provinces of Canada during the COVID-19 pandemic. The main outcome of the study was nurses' intention to quit within two years due to lack of job satisfaction. The mode of healthcare delivery was categorized into; in-person, online, or blended. Multivariable logistic regression was performed to examine the association between mode of healthcare delivery and intention to quit job after adjusting for sociodemographic, job-, and health-related factors. Analysis for the present study was restricted to 3,430 nurses, weighted to represent 353,980 Canadian nurses. Intention to quit job, within the next two years, due to lack of satisfaction was reported by 16.4% of the nurses. Results showed that when compared to participants who provided in-person healthcare services, those who delivered online or blended healthcare services were at decreased odds of intention to quit their job due to lack of job satisfaction (OR = 0.47, 95% CI: 0.43-0.50 and OR = 0.64, 95% CI: 0.61-0.67, respectively). Findings from this study can inform interventions and policy reforms to address nurses' needs and provide organizational support to enhance their retention and improve patient care during times of crisis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".