How Much Distress Is Too Much on Deployed Operations? Validation of the Kessler Psychological Distress Scale (K10) for Application in Military Operational Settings
Why this work is in the frame
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Bibliographic record
Abstract
The aim of this study was threefold: (a) to assess the factor structure of the Kessler Psychological Distress Scale (K10) to determine whether interpreting the scale as a single dimensional measure of psychological distress is justified in military operational setting; (b) to validate the K10 for mental health surveillance in operational settings against self-reported occupational impairment; (c) to evaluate whether the K10 has better discriminatory power than de facto standards for mental health surveillance on deployment, namely the Patient Health Questionnaire and the Posttraumatic Stress Disorder Checklist, Civilian version. A convenience sample of Canadian Armed Forces personnel serving in Afghanistan (N = 1,264) completed self-report measures of psychological distress and occupational impairment. On examination of 6 competing models, the authors determined that interpreting the K10 as a measure of unspecified psychological distress is justified. Using receiver operating characteristic (ROC) curve analysis, they identified new cutoff values for dichotomous and polychotomous scoring methods. After comparing the area beneath the ROC curves for each of the 3 mental health surveillance questionnaires, the authors determined that all measures perform well as predictors of self-rated occupational impairment, with values ranging from .86 to .90. These results highlight the importance of cross-setting validation and demonstrate that validating psychological screening questionnaires against self-report measures of occupational impairment can be a useful strategy for understanding the manifestation of psychological distress on deployed military operations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it