The status and affecting factors of anxiety and depression in Chinese warship servicemen
Bibliographic record
Abstract
Objective To investigate the status and its affecting factors of anxiety and depression in warship servicemen.Methods 453 male warship servicemen were selected by cluster random sampling and evaluated with the StateTrait Anxiety Inventory(STAI),Beck Depression Inventory(BDI),20-item Toronto Alexithymia Scale(TAS-20) Chinese version,Coping Style Questionnaires(CSQ),Social Support Rating Scale(SSRS),Personal Evaluation Inventory(PEI) and General Information Questionnaire.Results The State Anxiety Inventory(S-AI) scores(40.88 ±9.17) and Trait Anxiety Inventory(T-AI) scores(41.02 ± 8.32) had no significant difference compared to the national norm(P 0.05).28.7% were selected with BDI≤4,43.3% were selected with 5≤BDI≤13,17.0% were selected with 14≤BDI≤20,and 11.0% were selected with BDI ≥21.The STAI and BDI scores were significantly positively related to the TAS,immature coping style and mixed coping style scores(r = 0.125 ~ 0.535,P 0.01),and significantly negatively related to the mature coping style,SSRS and PEI scores(r =-0.167 ~-0.462,P 0.01).The result of stepwise analysis showed that significant factors affecting S-AI scores were the scores of T-AI,BDI,F1(TAS) and Utilization of Support.Factors affecting T-AI scores were the S-AI,BDI,Fantasy,PEI,Problem-solving and Objective Support scores.Factors affecting BDI scores were the Self-accusation,T-AI,S-AI,Problem-solving,PEI,F1 scores and Education Years.Conclusion The warship servicemen showed normal anxiety level compared to the general group and the detection rate of depression was28.0% with a BDI cut-off score 14.Alexithymia,social support,coping style and personal evaluation were the main influencing factors of anxiety and depression in warship servicemen.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".