Stress, resilience, and moral distress among health care providers in oncology during the COVID-19 pandemic
Bibliographic record
Abstract
Background The coronavirus pandemic has potential implications for stress levels and resilience among oncology healthcare professionals (HCPs). This study aims to assess perceived stress, resilience, and moral distress levels among oncology HCPs in Jordan during the pandemic and identify associated risk factors. Methods An online cross-sectional survey was conducted among oncology HCPs in Jordan using three validated tools: Perceived Stress Scale (PSS), Connor-Davidson Resilience Scale (CD-RSIC), and Moral Distress Thermometer (MDT). Seven items were used to assess sources of stress. Results A total of 965 participants enrolled with a 74% response rate. The participants’ ages ranged from 20 to 74 (mean = 32.74, SD = 5.197), with 79.1% males, 45.1% were physicians, 32.6% were public hospital workers, 57.1% were married, and 56.6% had children below 18 years. Findings indicated moderate perceived stress (Mean = 15.87, SD = 5.861), low resilience (Mean = 29.18, SD = 5.197), and high moral distress (Mean = 4.72, SD = 2.564). Females, unmarried individuals, and younger age groups exhibited higher PSS (p = 0.009, p < 0.001, and P<0.001) and lower resilience (p = 0.024, p = 0.034, and p = 0.001). Not having children below 18 years correlated with higher perceived stress (P < 0.001). In linear regression analysis, age and gender emerged as significant predictors of both perceived stress and resilience. Female participants reported stress related to the risk of contracting COVID-19 (p = 0.001), transmitting it to others (p = 0.017), social isolation (P < 0.001), and having children at home due to school closures (p = 0.000). A cohort of 239 participants repeated the survey within a two-month interval, revealed a statistically significant decrease in the CD-RISC scores (p < 0.001). Conclusion Oncology HCPs in Jordan experienced moderate stress, high moral distress, and poor resilience during the COVID-19 pandemic. These factors may negatively affect the quality of oncology care. Urgent measures are necessary to support HCPs in coping with unforeseen circumstances in the future.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".