Relationship between Alexithymia,Coping Styles and Passive Psychology in Nurses
Bibliographic record
Abstract
Objective To explore the relationship of alexithymia to passive psychology and the mediating effect of coping styles in it.Methods Toronto Alexithymia Scale(TAS-20),Trait Coping Stule Questionnaire(TCSQ) and Genera Health Questionnaire(GHQ-20) were used to perform a questionnaire in 503 nurses.Results(1)Alexithymia and passive coping styles were positively correlated with passive psychology(P0.01),active coping styles negatively with passive psychology(P0.01).(2) The model fit indices of coping styles as mediating variables in alexithymia and passive psychology wereχ2/df=1.459,RMSEA=0.030,CFI=0.995,IFI=0.995,RFI=0.966,TCI=0.989,NEI=0.985,respectively.The models fitted well.Alexithymia and coping styles explained 99% of total variance of passive psychology,mediating effect accounting for 94.59% of total effect.Conclusion Alexithymia and Coping Style are important factors influencing negative psychology of nurses.Coping style plays an important mediating role in influence of alexithymia on passive psychology.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".