Classifying childhood war trauma exposure: latent profile analyses of Sierra Leone’s former child soldiers
Bibliographic record
Abstract
BACKGROUND: Former child soldiers are at elevated risk for mental health problems (e.g., traumatic stress, emotion dysregulation, and internalizing and externalizing problems). To examine which groups of former child soldiers are more likely to have difficulties with emotion regulation, interpersonal relationships, and mental health postconflict, we explored patterns of war trauma exposure and their effects on subsequent mental health problems among former child soldiers in Sierra Leone. METHODS: Participants were 415 (23.86% female) Sierra Leonean former child soldiers participating in a 15-year, four-wave longitudinal study. At T1 (2002), 282 former child soldiers (aged 10-17) were recruited. T2 (2004) included 186 participants from T1 and an additional cohort of self-reintegrated former child soldiers (NT2 = 132). T3 (2008) and T4 (2016/2017) participants were youth enrolled in previous waves (NT3 = 315; NT4 = 364). Latent profile analysis (LPA) was used to classify participants based on the first-time reports of eight forms of war exposure (separation and loss of assets, parental loss, loss of loved ones, witnessing violence, victimization, perpetrating violence, noncombat activities, and deprivation). ANOVA examined whether patterns of war exposure were associated with sociodemographic characteristics and mental health outcomes between T1 and T4. RESULTS: LPA identified two profiles: higher exposure versus lower exposure, using cumulative scores of eight forms of war-related trauma exposure. The 'higher war exposure' group comprised 226 (54.5%) former child soldiers and the 'lower war exposure' group included 189 (45.5%). Significantly higher levels of violence-related and combat experiences characterized the group exposed to more traumatic events. The 'higher war exposure' group reported more PTSD symptoms at T2, more hyperarousal symptoms across all waves, and more difficulties in emotion regulation at T4. CONCLUSIONS: Former child soldiers exposed to higher levels of war-related traumatic events and loss should be prioritized for mental health services immediately postconflict and as they transition into adulthood.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".