Convergent and Discriminant Validity of the Blast Exposure Threshold Survey in United States Military Service Members and Veterans
Bibliographic record
Abstract
The Blast Exposure Threshold Survey (BETS) is a recently developed and promising new self-report measure of lifetime blast exposure (LBE). However, there are no studies that have examined the psychometric properties of the BETS, which currently limits its clinical utility. The purpose of this study was to examine the convergent and discriminant validity of the BETS by comparing the BETS Generalized Blast Exposure Value (GBEV) to six variables hypothesized to be associated with LBE (i.e., single-item LBE, combat exposure, years in the military, number of combat deployments, and military occupation specialty [MOS]) and three variables hypothesized not to be associated with LBE (i.e., age at the time of injury, estimated pre-morbid Full-Scale Intelligence Quotient [FSIQ], and resilience). Participants were 202 United States service members and veterans prospectively enrolled from three military medical treatment facilities (68.7%) and via community recruitment initiatives (31.3%). Participants completed the BETS, Combat Exposure Scale (CES), Deployment Risk and Resiliency Inventory-2 Combat Experiences (DRRI-2 CE), Traumatic Brain Injury-Quality of Life Resilience scale, and a brief structured interview. For some analyses, participants were classified into two blast risk MOS groups: high ( n = 89) and low ( n = 94). The BETS GBEV was not significantly correlated with all three non-blast related variables (r s = 0.01 to r s = -0.12). In contrast, GBEV was significantly ( p < 0.001) associated with all blast-related variables; single-item LBE (r s = 0.76), CES (r s = 0.58), number of combat deployments (r s = 0.53), DRRI-2 CE (r s = 0.48), and high blast risk MOS (r = 0.36, medium effect size). However, a stronger relationship was found between the blast-related variables and three modified GBEV scores when excluding some small weapons categories; single-item LBE (r s = 0.80–0.82), CES (r s = 0.64–0.67), number of combat deployments (r s = 0.56), DRRI-2 CE (r s = 0.51–0.53), and high blast risk MOS (r = 0.42–0.49, medium-large effect size). This is the first study to examine the psychometric properties of the BETS. Overall, these results offer support for the convergent and discriminant validity of the BETS. In order to ensure that the BETS can be confidently used as a valid and reliable measure of LBE, more research is needed to further examine the psychometric properties of the test, particularly with regard to the establishment of test-retest reliability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".