Empathy among Medical Students Studying Different Curricula: A Comparative Study
Bibliographic record
Abstract
Objective: To determine and compare empathy among medical students, studying two different curricula.Study Design: Cross Sectional.Place and Duration of Study: The study was carried out at the Department of Community Medicine of WahMedical College, Wah Cantt from January 2018 to June 2018.Materials and Methods: The study was carried out on second and fourth year MBBS students. The sample sizewas 90, calculated by Open Epi calculator and the students were selected by using stratified random samplingtechnique. A data collection tool comprised of two parts; demographic information about the individual wascollected in the first part and second part was based on Toronto Empathy Questionnaire. The questionnaire had16 questions and scored between 0-64. The questions were responded on a Likert scale of never, rarely,sometimes, often and always. The data was analyzed by using software program of SPSS version 20. Descriptivestatistics and Mann-Whitney U test was applied to compare the mean scores of empathy of students studyingdifferent curricula.Results: Mean score of empathy among students was 42.89+ 8.535. Mean empathy score of 2 year studentsth nd was 45.58 +7.203 and 4 year students came out to be 40.20 +8.981. Empathy was statistically significant in 2 year students and in female students; female students' empathy score was 46.38 while male students score was 39.40.Conclusion: It is concluded that 2 year students who studied integrated curriculum showed higher empathyth scores than 4 year students studying traditional curriculum. Moreover, female students showed significantlyhigher empathy scores as compared to the male students.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".