Bibliographic record
Abstract
目的分析急诊科护士述情障碍与职业倦怠的相关性,探讨急诊科护士述情障碍对职业倦怠的影响。方法采用一般资料调查表、多伦多述情障碍量表(20-item Toronto alexithymia scale,TAS-20)和Maslach职业倦怠量表(Maslach burnout iaventorygeneral suruey,MBI-GS)对5省市22所医院急诊科护士进行问卷调查,采用分层回归分析探讨急诊科护士述情障碍对职业倦怠的影响。结果共调查464名急诊科护士,21.6%(100人)存在述情障碍,45.0%(209人)存在可疑述情障碍。Pearson相关分析显示,急诊科护士述情障碍各因子得分与职业倦怠各因子得分均呈显著正相关(均P<0.01)。分层回归分析显示,情感识别障碍和外向型思维为急诊科护士职业倦怠的危险因素。结论急诊科护士述情障碍问题突出,述情障碍与职业倦怠呈正相关关系,护理管理者可通过提供针对性的述情障碍干预措施以缓解急诊科护士职业倦怠状况。
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".