Trauma exposure and psychometric properties of the life events checklist among adults in South Africa
Bibliographic record
Abstract
Background: Trauma exposure is widespread and linked to chronic physical and mental health conditions including posttraumatic stress disorder. However, there are major gaps in our knowledge of trauma exposure in Africa and on the validity of instruments to assess potentially life-threatening trauma exposure.Objective: The Life Events Checklist for the DSM-5 (LEC-5) is a free, widely used questionnaire to assess traumatic events that can be associated with psychopathology. As part of a case–control study on risk factors for psychosis spectrum disorders, we used the LEC-5 to examine the frequency of traumatic events and to assess the questionnaire’s factor structure in South Africa (N = 6,765).Method: The prevalence of traumatic events was measured by individual items on the LEC-5 across the study sample, by case–control status, and by sex. Cumulative trauma burden was calculated by grouping items into 0, 1, 2, 3, and ≥4 traumatic event types. Psychometric properties of the LEC-5 were assessed through exploratory and confirmatory factor analyses.Results: More than 92% of the study sample reported experiencing ≥1 traumatic event; 38.7% reported experiencing ≥4 traumatic event types. The most endorsed item was physical assault (65.0%), followed by assault with a weapon (50.2%). Almost 94% of cases reported ≥1 traumatic event compared to 90.5% of controls (p < .001) and 94% of male participants reported ≥1 traumatic event compared to 89.5% of female participants (p < .001). Exploratory factor analysis revealed a 6-factor model. Confirmatory factor analyses of three models found that a 7-factor model based on the South African Stress and Health survey was the best fit (standardized root mean square residual of 0.024, root mean square error of approximation of 0.029, comparative fit index of 0.910).Conclusion: Participants reported very high exposure to traumatic events. The LEC-5 has good psychometric priorities and is adequate for capturing trauma exposure in South Africa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".