Assessment of Psychological Variables amongst Indian Medical Professionals: A Cross-sectional Study
Bibliographic record
Abstract
Background: The doctor–patient relationship is of critical importance to patient satisfaction and is impacted by various doctor-related factors. Aim: To assess the levels of emotional intelligence (EI), empathy, everyday/perceived discrimination and verbal aggression amongst medical professionals and medical students, and to understand the interrelations between these variables and their differences across groups. Materials and Methods: This cross-sectional study included convenience sampling of 191 medical students, and 94 medical professionals (residents and attending doctors). They were administered the Wong and Law emotional intelligence scale, Toronto empathy questionnaire, everyday discrimination scale and verbal aggression sub-scale from the Buss–Perry aggression scale. Data was analysed using Statistical Package for Social Sciences 20. Results: EI was significantly greater amongst professionals as compared to students, and positively correlated to years of experience in the medical profession. Everyday discrimination increased with years of experience in the medical fraternity and was also negatively correlated with the ‘emotion regulation’ component of EI. Female participants had higher levels of empathy and lower levels of everyday discrimination. Conclusion: In Indian medical professionals the levels of EI increase with years of experience and are higher for medical professionals than students. The levels of perceived discrimination increase with years of experience and were greater for medical professionals and male doctors. Perceived discrimination and verbal aggression showed a negative association with empathy and EI. Understanding the factors that impact the doctor–patient relationship, as well as the doctor’s personal experience in the medical fraternity, are crucial to improve patient satisfaction, as well as to improve the well-being of the medical professionals.
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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 | 0.000 |
| 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".