Study on The Correlation between Alexithymia and Social Support of Nurses in ICU
Bibliographic record
Abstract
Objectives: The goal of the present study is to investigate the status of alexithymia and social support among ICU nurses，and to investigate the correlation between the alexithymia and social support of ICU nurses. Methods: From June to November 2016，400 ICU nurses were sampled from 7 general hospitals in Changchun with convenient sampling method， there were 375 valid questionnaires. The efficiency was 93.75%. The questionnaire includes the self-designed general situation questionnaire，Toronto Alexithymia Scale (TAS-20) and Social Support Rating Scale (SSRS). Results: 1. The total score of the alexithymia of ICU nurses was 55.07± 7.85，and the mean score of alexithymia was 2.75±0.39. 2. The total score of social support of ICU nurses was 37.14± 6.75，and the mean score of social support was 2.86±0.52. 3. The total score of the alexithymia was negatively correlated with the subjective support，objective support，supportive utilization and the total score of social support(P<0.01). Conclusions: 1. ICU nurses generally have a certain degree of empathy， and at a moderate level. The job title，working years，with or without children， marital status were the factors that affect the ICU nurses9 alexithymia. 2. The social support of ICU nurses was at the middle level. Gender，age， job title，working years，with or without children and marital status were the factors affecting the social support of ICU nurses. 3. The alexithymia of ICU nurses was negatively correlated with the subjective support.
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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 | 0.000 | 0.003 |
| 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".