Recommendations for a Standard Research Assessment of Insomnia
Bibliographic record
Abstract
STUDY OBJECTIVES: To present expert consensus recommendations for a standard set of research assessments in insomnia, reporting standards for these assessments, and recommendations for future research. PARTICIPANTS: N/A. INTERVENTIONS: N/A. METHODS AND RESULTS: An expert panel of 25 researchers reviewed the available literature on insomnia research assessments. Preliminary recommendations were reviewed and discussed at a meeting on March 10-11, 2005. These recommendations were further refined during writing of the current paper. The resulting key recommendations for standard research assessment of insomnia disorders include definitions/diagnosis of insomnia and comorbid conditions; measures of sleep and insomnia, including qualitative insomnia measures, diary, polysomnography, and actigraphy; and measures of the waking correlates and consequences of insomnia disorders, such as fatigue, sleepiness, mood, performance, and quality of life. CONCLUSIONS: Adoption of a standard research assessment of insomnia disorders will facilitate comparisons among different studies and advance the state of knowledge. These recommendations are not intended to be static but must be periodically revised to accommodate further developments and evidence in the field.
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 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 | 0.337 | 0.487 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| Bibliometrics | 0.035 | 0.029 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.019 | 0.010 |
| Research integrity | 0.013 | 0.024 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".