Assessing trauma and posttraumatic stress disorder: Single, open-ended question versus list-based inventory.
Bibliographic record
Abstract
Trauma exposure is a precursor to a diagnosis of posttraumatic stress disorder (PTSD). A dearth of empirical evidence exists on the impact of different measurement practices on estimates of trauma exposure and PTSD within representative epidemiological samples. In the present study, we examined differences in reported trauma exposure and rates of PTSD using single, open-ended question versus list-based trauma assessments in a general community sample. Using data from the third wave of the Montreal epidemiological catchment area study (N = 1029), participants were interviewed in person by a lay interviewer about lifetime history of trauma exposure and PTSD. Prevalence rates of trauma exposure and PTSD diagnosis using single, open-ended question and list-based assessment were compared using a within-subject design. A single, open-ended question versus list-based trauma assessment yielded trauma-exposure rates of 61%, 95% CI [57.8, 63.8] and 78%, 95% CI [75.2, 80.3], respectively. Conditional rates of lifetime PTSD decreased from 6.7%, 95% CI [5.8, 9.4] to 6%, 95% CI [4.4, 7.7], respectively. Increases in trauma exposure were more pronounced in women (33.7%) than men (21.5%), as well as in the younger stratum of study participants (15-24 years old; 36.1%). Underestimation of PTSD using a single, open-ended question assessment was minimal, although all missing cases were women. Our results lend support to the importance of using comprehensive assessments of exposure to potentially traumatic events when conducting epidemiological research, especially when reporting conditional rates of PTSD. Previous research may have underestimated the prevalence of trauma exposure, particularly among young women. (PsycINFO Database Record
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".