The Relationship between Alexithymia, Empathy and Burnout Levels Amongst Intensive Care Nurses During the COVID-19 Pandemic
Bibliographic record
Abstract
Objective: The study aims to determine the relationship between in intensive care nurses’ alexithymia, empathic tendency, and burnout levels in the COVID-19 pandemic process.Materials and Methods: The cross-sectional and correlational study was conducted with 170 intensive care nurses. Data were collected with Nurse Information Form, Toronto Alexithymia Scale 20, Empathic Tendencies Scale and Maslach Burnout Inventory between February and March 2021 as online.Results: Of the ICU nurses participating in the study, 81.2% were female, mean age were 29.9±6.1 years, 67.6% had bachelor’s degree and 46.5% of them were working in the pandemic ICU. The mean alexithymia was 51.4±10.7 and a quarter of the nurses were alexithymic. Empathic tendencies levels were slightly above average and burnout subdimensions mean were medium. Multiple regression analysis results show that the built regression model is statistically significant (F= 36.4, p< 0.001). Empathy and burnout levels could explain 30% of their alexithymia levels. The ICU nurses’ empathy levels predicted alexithymia levels negatively (β= -0.29, p< 0.001), and burnout levels predicted alexithymia levels positively (β=35, p< 0.001) to a statistically significant degree.Conclusion: It was concluded that intensive care nurses’ the alexithymia scores have a negative correlation with empathic tendencies and a positive correlation with burnout.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".