Validation of the Insomnia Severity Index in Primary Care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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) | 0.000 | 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 | 0.001 |
| 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