Association of Empathy and Stress in Medical Professionals
Bibliographic record
Abstract
Objective: To find association between perceived stress and empathy in medical professionals. Study Design: A descriptive cross sectional study Place and duration of study: RMU’s affiliated hospitals Rawalpindi, 1 year duration from June 2019 to June 2020. Methodology: A total of 178 sample size was calculated by using WHO sample size calculator and non-probability purposive sampling was applied . A validated standardized closed ended questionnaire was used having three parts i.e part A was related to demographic details of participants. ,part B was related to evaluation of empathy scores by using The Toronto Empathy Questionnaire and part C related to Cohen Perceived stress score. Participants were divided into five groups i.e. nurses, house officers, residents, physicians and surgeons Data was entered and analyzed in SPSS 22 and correlation between empathy and perceived stress was calculated by Spearmans correlation formula and a p-value of <0.05 was considered significant. Results: Mean age of respondents was 34.57±8.10 with 60.1% females and 39.9% males. A total of 178 medical professionals; nurses 19 (10.7%), house officers 37 (20.8%), residents 34 (19.1%), physicians 48 (27.0%), surgeons 40 (22.5%).Following the data calculated, below average empathy was noted in 33.1% respondents , average empathy in 41.6% and above average in 25.3%, As high empathic attitude was noted in physicians and surgeons in comparison to other professionals. the data calculated about perceived stress across all participants, 25.3% reported low stress, 47.2% moderate stress and 27.5% high stress and we can appreciate that low stress scores were calculated in physicians and surgeons in comparison to other professionals.A negative correlation between empathy and perceived stress was calculated. Conclusion: Our study shows a significant proportion of nurses, house officers and residents had a comparatively higher stress scored and hence decreased empathy whereas physicians and surgeons had a comparatively lower stress and therefore showed high empathic attitude, furthermore it indicates a negative correlation between perceived stress and empathy.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".