The association of psychological symptoms with unintentional injuries among retired employees of a university in China
Bibliographic record
Abstract
To investigate the association of psychological symptoms with injury risk, psychological symptoms were measured using symptom checklist-90 revised (SCL-90-R) and the unintentional injury information was followed up for 1 year among retired employees at a university in China. The injury rate had a significant difference between groups of raw mean score > or =2.0 and <2.0 for SCL-90-R global factor and subscale factors of obsessive compulsiveness, interpersonal sensitivity, depression and anxiety. After accounting for the factors of daily housework, physical activities, living alone and demographic factors, SCL-90-R global factor (odds ratio (OR) = 1.87, 95% CI: 1.20-2.91) and subscales factors of obsessive compulsiveness (OR = 1.93, 95% CI: 1.31-2.85), interpersonal sensitivity (OR = 2.05, 95% CI: 1.09-3.02), depression (OR = 2.09, 95% CI: 1.40-3.12) and anxiety (OR = 1.58, 95% CI: 1.03-2.44) were still significantly associated with an elevated risk of unintentional injury among the retired employees. In order to reduce the risk of unintentional injuries among the elderly, a psychological health service should be provided in the community.
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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 | 0.001 |
| 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".