Interdisciplinary approaches to understand traumatic stress as a public health problem
Bibliographic record
Abstract
In November 2016 researchers, clinicians, and student experts gathered in Dallas, Texas, USA for the annual meeting of the International Society for Traumatic Stress Studies (ISTSS). From the description of the meeting theme forward it was acknowledged that interdisciplinary approaches will be required to fully understand traumatic stress as a public health problem, including epidemiology, biostatistics and health services research. Further, it was recognized that knowledge translation would be of critical importance if we were to increase public awareness of - and destigmatize - posttraumatic biopsychosocial problems, including posttraumatic stress disorder (PTSD). Moreover, we recognized innovative technologies as potentially usefully applied to public health strategies, including media and internet usage might aid knowledge translation and have a direct impact on help-seeking and trauma-informed care. The present special issue of the journal brings a collection of papers from some of the most impactful and innovative ideas that were presented in Dallas.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".