Empathy among medical students: A cross-sectional survey.
Bibliographic record
Abstract
BACKGROUND: Empathy is regarded as one of the leading elements of good physician-patient relationships, having a strong association with improved patient outcomes. It is an aspect of personality, which is considered to be an essential component in the development of interpersonal understanding and in assisting proficiency in communication. Developing empathy among medical students who are going to become tomorrow's physicians is an important part of medical education. The objective of this study was to measure the mean empathy score of medical students at a private medical college and identify associated factors. METHODS: This cross-sectional study was conducted from January to June 2018 among 569 medical students at a private medical college in Lahore. Toronto empathy questionnaire (TEQ) was used to determine empathy levels among students. Data was analysed in IBM SPSS-20. Mean TEQ scores were calculated. Independent sample t-test and one-way ANOVA were used to determine association between mean Toronto empathy score and socio-demographic variables, significant at a p-value of <0.05. RESULTS: The age of 569 respondents ranged from 17 to 27 years with a mean of 20.82±1.757. There were 317 (55.71%) females; and 312 (54.8%) were residing at home with parents. The overall mean TEQ score was calculated to be 42.57±7.513. The Cronbach's alpha was 0.710. There was a significant association between empathy levels and year of study of the respondents (p<0.001), gender (p<0.001) and students' permanent area of residence (p=0.018). CONCLUSION: Overall mean TEQ score was found to be 42.57±7.513. Statistically significant difference was found between gender, place of residence, year of study and mean empathy scores.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".