Global Psychotrauma Screen (GPS): psychometric properties in two Internet-based studies
Bibliographic record
Abstract
Background: Potentially traumatic stressors can lead to various transdiagnostic outcomes beyond PTSD alone but no brief screening tools exist for measuring posttraumatic responses in a transdiagnostic manner.Objective: Assess the psychometric characteristics of a new 22-item transdiagnostic screening measure, the Global Psychotrauma Screen (GPS).Method: An internet survey was administered with English speaking participants recruited passively via the website of the Global Collaboration on Traumatic Stress (GC-TS) (nGC-TS = 1,268) and actively via Amazon’s MTurk (nMTurk = 1,378). Exploratory factor analysis, correlational analysis, sensitivity and specificity analysis, and comparisons in response between the two samples and between male and female respondents were conducted.Results: Exploratory factor analysis revealed a single factor underlying symptom endorsements in both samples, suggesting that such problems may form a unitary transdiagnostic, posttraumatic outcome. Convergent validity of the GPS symptom and risk factors was established with measures of PTSD and dissociative symptoms in the MTurk sample. Gender differences were seen primarily at the item level with women more often endorsing several symptoms and specific risk factors in the MTurk sample, and the GC-TS recruited sample endorsed more symptoms and risk factors than the MTurk sample, suggesting that the GPS may be sensitive to group differences. A GPS symptom cut-off score of 8 identified optimized sensitivity and specificity relative to probable PTSD based on PCL-5 scores.Conclusions: The current results provide preliminary support for the validity of the GPS as a screener for the concurrent measurement of several transdiagnostic outcomes of potentially traumatic stressors and the apparent unifactorial structure of such symptoms is suggestive of a single or unitary posttraumatic outcome. Future research is needed to evaluate whether similarly strong psychometric properties can be yielded in response to completion of the GPS in other languages.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".