HUBUNGAN EMPATI DENGAN HASIL UJIAN KETERAMPILAN KOMUNIKASI DOKTER-PASIEN DAN BREAKING BAD NEWS
Bibliographic record
Abstract
Empathy is the ability to understand someone else’s experiences, emotions, and feelings. Empathy is one of the professional qualities in patient-centered health services. Empathy in medical education is closely related to the student's mastery of clinical competence. The student’s empathy measurement becomes crucial in acquiring feedback regarding empathy learning at the preclinical education stage. It encourages the study of the relationship between empathy and doctor-patient communication and breaking bad news (BBN) skill examination results. This study used a cross-sectional design on Faculty of Medicine students of Universitas Tarumanagara. Empathy was assessed using The Toronto Empathy Questionnaire while the results of the doctor-patient communication and BBN skills examinations were collected from the respondents via Google Form. In this study, the number of respondents was as many as 124 students with the majority of them being female (71.8%). A total of 78.2% of respondents possessed high empathy. A total of 94.4% of respondents passed the doctor-patient communication skills examination and 98.4% of respondents passed the BBN skills examination. Respondents who passed both of the examinations were 92.7%. Empathy and the results of the two clinical skills examinations were analyzed using Fisher's exact test which led to the discovery of a p-value >0.05. In this study, it can be concluded that there is no significant relationship between empathy and the results of the doctor-patient communication and BBN skills examinations on Faculty of Medicine students of Universitas Tarumanagara. Keywords: empathy, clinical skills, medical students Abstrak Empati merupakan kemampuan untuk memahami pengalaman, emosi, dan perasaan orang lain. Empati termasuk salah satu kualitas profesionalisme dalam pelayanan kesehatan yang berpusat pada pasien. Empati pada tahap pendidikan kedokteran berkaitan erat dengan penguasaan mahasiswa terhadap suatu kompetensi klinis. Pengukuran tingkat empati mahasiswa menjadi hal yang penting sebagai umpan balik terhadap pembelajaran empati pada tahap pendidikan pre-klinik. Hal ini mendorong untuk dilakukannya penelitian mengenai hubungan empati dengan hasil ujian keterampilan komunikasi dokter-pasien dan breaking bad news (BBN). Penelitian ini menggunakan desain cross sectional terhadap mahasiswa Fakultas Kedokteran Universitas Tarumanagara. Empati dinilai dengan menggunakan The Toronto Empathy Questionnaire serta hasil ujian keterampilan komunikasi dokter-pasien dan BBN ditanyakan ke responden melalui Google Form. Jumlah responden pada penelitian ini sebanyak 124 mahasiswa dengan mayoritas responden berjenis kelamin perempuan (71,8%). Sebanyak 78,2% responden memiliki empati tinggi. Sebanyak 94,4% responden lulus ujian keterampilan komunikasi dokter-pasien dan 98,4% responden lulus dalam ujian keterampilan BBN. Responden yang lulus pada kedua ujian keterampilan tersebut sebesar 92,7%. Empati dan hasil ujian kedua keterampilan klinis dianalisis dengan menggunakan uji Fisher’s exact dan didapatkan p value >0,05. Pada penelitian ini dapat disimpulkan bahwa tidak terdapat hubungan yang bermakna antara empati dengan hasil ujian keterampilan komunikasi dokter-pasien dan BBN pada mahasiswa Fakultas Kedokteran Universitas Tarumanagara.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.011 |
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".