A level of empathy and spirituality among undergraduate medical students of a tertiary hospital in Telangana
Bibliographic record
Abstract
Introduction: Patients' care with empathy has shown a higher clinical competence with great rapport. It leads to an accurate diagnosis with fewer medical errors. Patients tend to be more satisfied with improved outcomes both psychologically and pharmacologically. Empathy supports medical students to achieve capabilities essential for patient-centered care and in development of affective skill, manners, and personal as well as professional growth. Aims and Objectives: To assess the level of empathy among medical students and to assess the level of spiritual well-being and its relation with empathy. Materials and Methods: The cross-sectional study was carried out from January 2021 to March 2021 period. A total of 200 medical students were selected for the study, fifty from each year. Jefferson Scale of Physician Empathy Student version (JSPE-S), Toronto Empathy Questionnaire (TEQ), Spiritual Well-Being Scale (SWBS), and Demographic Questionnaire were used for the collection of data. Results: The mean JSPE-S score was 108.41 (14.19), mean TEQ score was 44.89 (6.26), and mean SWBS was 80.58 (18.89). By JSPE-S, the mean empathy score decreased from the 3 rd year and was lower in the final year ( P = 0.00002). By TEQ, the empathy score was higher in the 2 nd year followed by 3 rd and 1 st and was lower in the final year ( P = 0.002). Females had higher empathy than males ( P < 0.002 for JSPE-S and P < 0.00001 for TEQ). There was a significant positive relationship between spiritual well-being score with mean JSPE-S ( r = 0.4429, P = 0.0012) and TEQ score ( r = 0.5777, P = 0.00001). Conclusion: Medical students had an average level of empathy and spiritual well-being. Clinical empathy decreased from the 3 rd year and was lower in final-year students. Spiritual well-being had a positive significant relationship with empathy. There was a statistically significant association between mean empathy scores with demographic variables such as gender, parental education, habit of doing meditation, permanent residence area, and year of study.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".