Validation of the Insomnia Severity Index in Primary Care
Bibliographic record
Abstract
BACKGROUND: Although insomnia is a prevalent complaint with significant consequences on quality of life, health, and health care utilization, it often remains undiagnosed and untreated in primary care settings. Brief, reliable, and valid instruments are needed to facilitate screening for insomnia in general practice. This study examined psychometric indices of the Insomnia Severity Index (ISI) to identify individuals with clinically significant insomnia in primary care settings. METHODS: A sample of 410 patients recruited from 6 general medical clinics completed the ISI before their appointment with a primary care physician. A subsample of 101 individuals also completed a semistructured clinical interview by telephone to determine the presence or absence of an insomnia disorder. Reliability and validity indices were computed, as was the discriminative capacity of each individual item. Convergence between ISI total score and the diagnosis derived from the interview was investigated. Receiver operator characteristic analyses were used to determine the optimal ISI cutoff score that correctly identified individuals with an insomnia disorder. RESULTS: ISI internal consistency was excellent (Cronbach α = 0.92), and each individual item showed adequate discriminative capacity (r = 0.65-0.84). The area under the receiver operator characteristic curve was 0.87 and suggested that a cutoff score of 14 was optimal (82.4% sensitivity, 82.1% specificity, and 82.2% agreement) for detecting clinical insomnia. Agreement between the ISI cut score and the diagnostic interview was moderate (κ = 0.62). CONCLUSIONS: These findings suggest that the ISI is a valid screening instrument for detecting insomnia among patients consulting in primary care settings.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".