The Interplay Of Stress & Empathy Among Healthcare Professionals During The Covid-19 Pandemic
Bibliographic record
Abstract
Background: Empathy is an essential and fundamental quality necessary for any healthcare professional to provide optimum patient care and function effectively as professional. As noted by Pflanz & Ogle (2006), stress, as a psychological phenomenon, can compromise both the performance and empathy of healthcare professionals. Aim: To examine the association between stress and empathy among healthcare professionals who were Covid-19 positive and to compare them with healthcare professionals who were not infected. Design: A quantitative study was conducted with a sample of 30 Covid positive and 30 Covid- negative healthcare professionals, chosen through purposive and snowball sampling methods. Results: The analysis revealed that the Perceived Stress Scale and Toronto Empathy Questionnaire gave the p-value of 0.0164 and p-value of 0.00062, respectively. Both results were significant at p<.05. Hence, there is a significant difference between the level of empathy and stress among healthcare professionals who were Covid positive and those who were not. However, there was no significant association between empathy and stress, with correlation coefficient (rs) of 0.215 and -0.09 for the two samples. Conclusion: Results suggest that there is a significant difference in empathy and stress between the two groups of healthcare professionals. The stress as well as empathy level was higher among those who were infected by the coronavirus, possibly due to the increased sensitivity to the suffering of others resulting from being infected themselves.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".