Screening for consequences of trauma – an update on the global collaboration on traumatic stress
Bibliographic record
Abstract
This letter provides an update on the activities of "The Global Collaboration on Traumatic Stress" (GC-TS) as first described by Schnyder et al. in 2017. It presents in further detail the projects of the first theme, in particular the development of and initial data on the Global Psychotrauma Screen (GPS), a brief instrument designed to screen for the wide range of potential outcomes of trauma. English language data and ongoing studies in several languages provide a first indication that the GPS is a feasible, reliable and valid tool, a tool that may be very useful in the current pandemic of the coronavirus disease 2019 (COVID-19). Further multi-language and cross-cultural validation is needed. Since the start of the GC-TS, new themes have been introduced to focus on in the coming years: a) Forcibly displaced persons, b) Global prevalence of stress and trauma related disorders, c) Socio-emotional development across cultures, and d) Collaborating to make traumatic stress research data "FAIR". The most recent theme added is that of Global crises, currently focusing on COVID-19-related projects.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".