The psychometric properties of the 10-item Kessler Psychological Distress Scale (K10) in Canadian military personnel
Bibliographic record
Abstract
The psychometric properties of the ten-item Kessler Psychological Distress scale (K10) have been extensively explored in civilian populations. However, documentation of its psychometric properties in military populations is limited, and there is no universally accepted cut-off score on the K10 to distinguish clinical vs. sub-clinical levels of distress. The objective of this study was to examine the psychometric properties of the K10 in Canadian Armed Forces personnel. Data on 6700 Regular Forces personnel were obtained from the 2013 Canadian Forces Mental Health Survey. The internal consistency and factor structure of the K10 (range, 0-40) were examined using confirmatory factor analysis (CFA). Receiver Operating Characteristic (ROC) analysis was used to select optimal cut-offs for the K10, using the presence/absence of any of four past-month disorders as the outcome (posttraumatic stress disorder, major depressive episode, generalized anxiety disorder, and panic disorder). Cronbach's alpha (0.88) indicated a high level of internal consistency of the K10. Results from CFA indicated that a single-factor 10-item construct had an acceptable overall fit: root mean square error of approximation (RMSEA) = 0.05; 90% confidence interval (CI):0.05-0.06, comparative fit index (CFI) = 0.99, Tucker-Lewis Index (TLI) = 0.99, weighted root mean square residual (WRMR) = 2.06. K10 scores were strongly associated with both the presence and recency of all four measured disorders. The area under the ROC curve was 0.92, demonstrating excellent predictive value for past-30-day disorders. A K10 score of 10 or greater was optimal for screening purposes (sensitivity = 86%; specificity = 83%), while a score of 17 or greater (sensitivity = 53%; specificity = 97%) was optimal for prevalence estimation of clinically significant psychological distress, in that it resulted in equal numbers of false positives and false negatives. Our results suggest that K10 scale has satisfactory psychometric properties for use as a measure of non-specific psychological distress in the military population.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".