A study of correlation between alexithymia and resilience in military personnel
Bibliographic record
Abstract
Objective To explore correlation between alexithymia and resilience in military personnel.Methods 2802 subjects were chosen by random cluster sampling to outline the military personnel resilience scale and military alexithymia scale.The data were analyzed by t-test,chi-square test,ANOVA,correlation analysis and multiple linear regressions analysis.Results The prevalence rate of alexithymia was 6.85％,while resilience was 6.60％.Compared to sea force (45.62 ± 11.25 ) and air force (45.32 ± 9.98 ) subjects,land force (47.92 ± 9.92)had significantly higher total score in alexithymia(P＜ 0.05 ).Compared to sea force (87.35 ± 12.15 ) and air force ( 88.58 ± 10.39 ) subjects,land force ( 85.73 ± 10.70) had significantly lower total score in resilience(P ＜ 0.05 ).The total score and every factor score of subjects with alexithymia were significant lower than those of control group in resilience(P ＜ 0.05 ).Every factor of Resilience Scale were mild negative correlation with every factor of Alexithymia Scale (P＜0.01).Problem solving,volition,optimism and family supports were the main factors which had affected on the alexithymia.Conclusion The prevalence rate of alexithymia in military personnel is lower than ordinary people,but there are still issues in alexithymia and resilience,particularly in land force.The research show mild correlation between alexithymia and resilience in military personnel. Key words: Military personnel; Resilience; Alexithymia; Correlation
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".