Examining Changes in Compassion Satisfaction, Burnout, Secondary Traumatic Stress and Work-Related Quality-of-Life of Rural Surgical and Obstetrical Nurses in British Columbia During the COVID-19 Pandemic
Bibliographic record
Abstract
Background This survey of rural nurses’ experiences is part of a program evaluation of the Rural Surgical and Obstetrical Networks (RSON), a five-year initiative (2018–2023) to strengthen and stabilize rural hospitals in British Columbia (BC), Canada. Purpose Our aim was to measure changes in professional and work-related quality-of-life of rural surgical and obstetrical nurses in eight communities across BC and determine if the RSON initiative mitigated impacts of the COVID-19 pandemic on nurses’ quality of life. Methods This longitudinal evaluation was administered via online surveys in 2021 and 2023. Work-related quality of life was measured with 23 items that assess job satisfaction, general wellbeing, work-life balance, stress level experienced at work, control, and working conditions. Professional quality of life was measured along three dimensions: compassion satisfaction (CS), burnout, and secondary traumatic stress (STS) (10 items each). Responses were linked by code and changes in quality of life were analyzed using paired Student's t-test. Results 107 nurses participated at time 1 and 28 at time 2. Burnout and secondary traumatic stress scores at time 1 were lower among older nurses and those with children. Over the two-year period (2021 to 2023), significant increases were observed in burnout ( p < 0.001), and secondary traumatic stress ( p = 0.04), while work-related Quality-of-Life decreased significantly ( p = 0.04). Compassion satisfaction decreased over time, though not statistically significant. Conclusions While the RSON initiative could not mitigate decreases in professional and work-related quality-of-life during COVID-19, it offered opportunities for clinical education and professional development among rural nurses.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".