Assessment of compassion and empathy in students of health care professions
Bibliographic record
Abstract
Compassion is one of the most important qualities expected and anticipated by patients from their healthcare professionals. Empathy is the ability to recognize and respond to others’ emotions. What differentiates compassion from empathy is the creation of a strong desire to reduce suffering. To assess and compare compassion levels in students of MBBS, BSc Nursing and Bachelors in Physiotherapy (BPT) in a medical college. This is a descriptive study done at the NRI Medical College in Guntur district of Andhra Pradesh state in India, The study involved students of 6 Semester MBBS (126), BSc Nursing 4 year (89) and 4 year BPT (48). The study instruments included the Compassion Score questionnaire & Toronto Empathy Questionnaire (TEQ) 3 which are both available freely in the public domain. Compassion scores in male students had a mean of 58.6 (range 22 to 78 and SD 9.2). Compassion scores in female students have a mean of 59.0 (range 29 to 80 and SD 9.1). Overall compassion scores were highest in MBBS students, both males and females when compared to Nursing and Physiotherapy students. Empathy scores also followed the same statistically significant pattern. Among compassion components, kindness was highest in BPT students while common humanity was higher in the medical students. MBBS students were seen to have significantly higher scores in all three negative components; indifference, separation and disengagement. It is proposed that the nature of training of nursing and physiotherapy students involves practical hands-on involvement in day to day care of their patients, while medical students are more involved in academic pursuits and less involvement with their patients during undergraduate training. Efforts must be made to encourage all healthcare students to inculcate empathy and compassion when communicating with patients. Empathy can be enhanced by adding training courses and workshops to the curriculum to teach empathy to develop interpersonal skills. There is a need to use innovative and creative approaches like simulation, role playing, storytelling and reflective discussion.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".