WELLBEING INDICATORS OF HEALTH CARE PROVIDERS AND THE INTENTION TO LEAVE THEIR POSITIONS: A CROSS-SECTIONAL STUDY FROM SASKATCHEWAN, CANADA, DURING THE COVID-19 PANDEMIC
Bibliographic record
Abstract
Background: The coronavirus disease 2019 (Covid-19) pandemic adversely affected health care providers’ (HCPs) wellbeing. This study explored the association between HCPs’ wellbeing indicators and the intention to leave their current position in the western Canadian province of Saskatchewan during the Covid-19 pandemic. Methods: A cross-sectional study was conducted among registered nurses (RNs), physicians, and respiratory therapists (RTs) between December 2021 and April 2022 via SurveyMonkey®. The online survey included demographics, validated scales to measure job satisfaction, burnout, moral distress, risk of depression, and resilience and a question about the HCPs’ intentions to leave their current position within the next year. Logistic regression models explored the association between the intention to leave the current position and HCPs’ wellbeing indicators. Adjusted odds ratios (AORs) and 95% confidence intervals (95%CI) were reported. Result: Of 1,497 participants, 38.6% considered leaving their positions within the next year. HCPs reported high levels of resilience and moral distress. However, HCPs were neither satisfied nor dissatisfied with their jobs. Additionally, 60.5% were at risk of depression, and 71.7% reported one or more symptoms of burnout. Controlling by gender, age group, having children, redeployment, burnout, and resilience levels, the odds of considering leaving the position decreased by 0.55 (95%CI 0.43-0.70) per unit of increase in the level of job satisfaction. HCPs experiencing high moral distress were more likely to leave their positions (AOR=3.97, 95%CI 2.93-5.39). RNs were more likely to consider leaving the position than physicians (AOR=1.68, 95%CI 1.13-2.50). Age interacted with gender, and burnout interacted with having children. Older women were more likely to leave the position than younger women. Although younger men were more likely to leave the position than men in the oldest age group. Moreover, the difference between those without and with children in the probability of considering leaving the position was wider among HCPs with no symptoms of burnout in comparison to the gap observed in the groups of HCPs with burnout. Conclusion: The level of job satisfaction is an indicator of HCPs’ retention. Distress levels and being RNs could predict HCPs’ intention to leave their positions. These findings could inform health care policies that enhance HCPs’ wellbeing and support initiatives that prevent high turnover rates during and after the pandemic.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".