Bibliographic record
Abstract
This study evaluated the familial incidence of sleep disturbances among individuals with insomnia complaints. The sample consisted of 285 patients evaluated for insomnia at a sleep disorders clinic. All patients completed a sleep survey and underwent a semistructured clinical interview as part of their initial evaluation of insomnia. Information on the presence and nature of sleep disturbances among their family members (first- and second-degree relatives) was obtained from a sleep survey. The findings indicate that 35% of patients consulting for insomnia had a positive family history of sleep disturbances. Insomnia was the most common type of sleep disturbance identified (76%) and the mother was the most frequently afflicted family member. Reports of sleep disturbances among a family member were more prevalent when the onset of insomnia was before 40-years-old than when it was later in life. A positive family history was slightly higher when the insomnia complaint involved sleep-onset difficulties relative to sleep-maintenance or mixed insomnias. Although the present findings suggest that a positive family history of insomnia may be a potential risk factor for insomnia, it is unclear whether this reflects a genetic predisposition or a social learning phenomenon.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".