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Record W3136330498 · doi:10.1016/j.sleep.2021.03.003

A very brief self-report scale for measuring insomnia severity using two items from the Insomnia Severity Index - development and validation in a clinical population

2021· article· en· W3136330498 on OpenAlexaff
Martin Kraepelien, Kerstin Blom, Erik Forsell, Nils Hentati Isacsson, Pontus Bjurner, Charles M. Morin, Susanna Jernelöv, Viktor Kaldo

Bibliographic record

VenueSleep Medicine · 2021
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsUniversité Laval
FundersStockholms Läns Landsting
KeywordsInsomniaPsychologyPopulationClinical psychologyPsychometricsCategorical variableScale (ratio)PsychiatryMedicineStatistics

Abstract

fetched live from OpenAlex

OBJECTIVE: To develop a very brief scale with selected items from the Insomnia Severity Index (ISI), and to investigate the psychometric properties of the proposed scale in a psychiatric sample. METHODS: Patient data from seven Cognitive Behavioral Therapy (CBT) for insomnia trials and from regular care were used in psychometric analyses (N = 280-15 653). The samples included patients screening (N = 6936) or receiving treatment (N = 1725) for insomnia and other psychiatric conditions. Six criteria relating to component structure, sensitivity to change and clinical representativeness were used to select items. Psychometric analyses for the proposed very brief scale were performed. RESULTS: = 0.86. With a cut-off of 6 points, the scale could detect Insomnia Disorder with a sensitivity of 84% and a specificity of 76%, which was close to the full ISI showing 86% and 80% respectively. CONCLUSIONS: The systematic psychometric evaluation based on a large sample from different contexts makes the proposed 2-item ISI version (ISI-2) a strong candidate for a very brief scale measuring insomnia, both for detecting cases and for measuring change during CBT with an overall high discriminative validity. ISI-2 is especially useful in clinical settings or population studies where there is a need to measure more than one condition at a time without overburdening patients. CLINICAL TRIALS: Trials used in this analysis: ClinicalTrials.gov identifier: NCT01105052 (https://www.clinicaltrials.gov/ct2/show/NCT01105052) (sample b), ClinicalTrials.gov identifier: NCT01256099 (https://clinicaltrials.gov/ct2/show/NCT01256099) (sample c and d), German clinical trial (DRKS), registration ID: DRKS00008745 (https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00008745) (sample e), ClinicalTrials.gov identifier: NCT01663844 (https://clinicaltrials.gov/ct2/show/NCT01663844) (sample f and g), ClinicalTrials.gov Identifier: NCT02743338 (https://clinicaltrials.gov/ct2/show/NCT02743338) (sample h).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.052
GPT teacher head0.351
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations63
Published2021
Admission routes1
Has abstractyes

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