Cognitive factors and processes in models of insomnia: A systematic review
Bibliographic record
Abstract
Cognition is central to the experience of insomnia. Although unhelpful thoughts about and around insomnia are a primary treatment target of cognitive behaviour therapy for insomnia, cognitive constructs are termed and conceptualised differently in different theories of insomnia proposed over the past decades. In search of consensus in thinking, the current systematic review identified cognitive factors and processes featured in theoretical models of insomnia and mapped any commonality between models. We systematically searched PsycINFO and PubMed for published theoretical articles on the development, maintenance and remission of insomnia, from inception of databases to February, 2023. A total of 2458 records were identified for title and abstract screening. Of these, 34 were selected for full-text assessment and 12 included for analysis and data synthesis following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We identified nine distinguishable models of insomnia published between 1982 and 2023 and extracted 20 cognitive factors and processes featured in these models; 39 if sub-factors were counted. After assigning similarity ratings, we observed a high degree of overlap between constructs despite apparent differences in terminologies and measurement methods. As a result, we highlight shifts in thinking around cognitions associated with insomnia and discuss future directions.
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".