Investigating alexithymia, empathy, and resilience in medical students during pandemic era: a cross-sectional study in northern Iran
Bibliographic record
Abstract
Abstract Background and aim Alexithymia is defined as emotional response inhibition. As well, empathy refers to the ability to put oneself in someone’s position and resilience is the capacity to recover from a series of negative emotional experiences. Considering the psychological distress induced by the coronavirus disease 2019 (COVID-19) pandemic together with academic stress and the role of empathy in physician–patient relationships, the present study was to investigate alexithymia, empathy, and resilience in Iranian medical interns and residents. Materials and methods This cross-sectional study was fulfilled in northern Iran in 2021–2022. In total, 394 medical interns and residents were initially recruited for this purpose. Then, an online sociodemographic survey form (SDSF), the Jefferson Scale of Empathy (JSE), the Toronto Alexithymia Scale (TAS-20), and the Connor–Davidson Resilience Scale (CD-RISC) were completed. The data analysis was performed using the IBM SPSS Statistics (ver.26) software in regard to the p < 0.05 significance level. Results The mean age of the study participants was 28.8 ± 5.00. As well, 38.1% of these individuals were male, 62.2% of the cases were single, and 54.6% of them were medical interns. The mean value of empathy, resilience and alexithymia was 89.90 ± 14.00, 49.75 ± 10.56, and 46.40 ± 16.40, respectively. No significant relationship was found between empathy and educational level (p = 0.532). As well, medical interns empathy and resilience than residents (p = 0.000 & p = 0.000, respectively). Besides, male participants had more empathy and resilience (p = 0.000 & p = 0.007). Conclusion Low empathy and resilience in medical interns and residents, especially in women who make up the majority of them, can be a warning for health care in Iran.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".