Posttraumatic stress symptom profiles of aid workers: Identifying risk and protective factors.
Bibliographic record
Abstract
OBJECTIVE: Researchers have documented elevated rates of posttraumatic stress disorder (PTSD) in aid workers. Yet, few have investigated the heterogeneity of PTSD presentations in this population. This study examined clinically relevant patterns of PTSD symptomatology in aid workers and examined whether factors such as the degree of trauma exposure (e.g., morally injurious events), social support, and sociodemographic and work characteristics predict symptom profiles. METHOD: Participants were 243 aid workers who had completed 8.2 assignments on average. They completed measures of trauma exposure, PTSD symptoms, and various types of social support. Latent profile analysis was used to identify PTSD symptom profiles using PCL-5 subscale scores. Next, profiles were compared on 15 potential risk and protective factors. RESULTS: Five profiles were identified: a no PTSD profile (49.4%), a low subclinical PTSD profile (21.8%), a dysphoric subclinical PTSD profile (5.8%), an intermediate clinical PTSD profile (14.8%), and a severe clinical PTSD profile (8.2%). Profiles differed in terms of witnessed traumatic events, morally injurious exposure, social support adequacy, age, number of assignments, types of assignments, and organizational support. CONCLUSIONS: This study is the first to identify distinct patterns of PTSD symptomatology in aid workers and to investigate novel psychological risk factors such as potentially morally injurious events. Overall, these findings provide further insight into the risk and protective factors for the psychological well-being of aid workers as well as avenues for improving the psychological assessment and support. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".