Prevalence and associated factors of alexithymia in intensive care unit nurses
Bibliographic record
Abstract
AIMS: The aim of this study was to investigate the incidence of alexithymia in intensive care unit nurses and determine the associated factors. DESIGN: A multi-center, cross-sectional study. METHODS: In total, 485 nurses in intensive care unit were recruited from 53 hospitals in China. Data collection tools used in the study included demographic characteristics, the Social Support Rating Scale (SSRS), Emotional Intelligence Scale (EIS), and the 20-item Toronto Alexithymia Scale (TAS-20). SPSS 25.0 software (Corp., Armonk, NY, USA) was used to preform data analysis. RESULTS: About 43.7% of intensive care unit nurses were classified as alexithymia in the whole sample (males: 50%, females: 43%). The median TAS-20 score was 60 (interquartile range = 9). The study found that alexithymia was significantly associated with marital status, whether living alone, working years, and social support (Adjusted R Squared = 0.194, F = 6.466, p < 0.01), while emotional intelligence was not statistically significant with alexithymia. CONCLUSIONS: Alexithymia is a psychological problem with high incidence in intensive care unit nurses. In this study, being unmarried or divorced, living alone, and having fewer years of work (≤5 years) were associated with a higher risk of alexithymia. Interventions that strengthen social support may also help improve the mental health of ICU nurses.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".