Short screening scales to monitor population prevalences and trends in non-specific psychological distress
Bibliographic record
Abstract
BACKGROUND: A 10-question screening scale of psychological distress and a six-question short-form scale embedded within the 10-question scale were developed for the redesigned US National Health Interview Survey (NHIS). METHODS: Initial pilot questions were administered in a US national mail survey (N = 1401). A reduced set of questions was subsequently administered in a US national telephone survey (N = 1574). The 10-question and six-question scales, which we refer to as the K10 and K6, were constructed from the reduced set of questions based on Item Response Theory models. The scales were subsequently validated in a two-stage clinical reappraisal survey (N = 1000 telephone screening interviews in the first stage followed by N = 153 face-to-face clinical interviews in the second stage that oversampled first-stage respondents who screened positive for emotional problems) in a local convenience sample. The second-stage sample was administered the screening scales along with the Structured Clinical Interview for DSM-IV (SCID). The K6 was subsequently included in the 1997 (N = 36116) and 1998 (N = 32440) US National Health Interview Survey, while the K10 was included in the 1997 (N = 10641) Australian National Survey of Mental Health and Well-Being. RESULTS: Both the K10 and K6 have good precision in the 90th-99th percentile range of the population distribution (standard errors of standardized scores in the range 0.20-0.25) as well as consistent psychometric properties across major sociodemographic subsamples. The scales strongly discriminate between community cases and non-cases of DSM-IV/SCID disorders, with areas under the Receiver Operating Characteristic (ROC) curve of 0.87-0.88 for disorders having Global Assessment of Functioning (GAF) scores of 0-70 and 0.95-0.96 for disorders having GAF scores of 0-50. CONCLUSIONS: The brevity, strong psychometric properties, and ability to discriminate DSM-IV cases from non-cases make the K10 and K6 attractive for use in general-purpose health surveys. The scales are already being used in annual government health surveys in the US and Canada as well as in the WHO World Mental Health Surveys. Routine inclusion of either the K10 or K6 in clinical studies would create an important, and heretofore missing, crosswalk between community and clinical epidemiology.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".