Bibliographic record
Abstract
Insomnia is a prevalent health complaint whose onset is precipitated by a variety of factors. There is an important need to identify and describe these factors to improve our understanding of risk factors and the natural history of insomnia. This article is aimed at identifying and describing the types of precipitating factors related to the onset of insomnia. A total of 345 patients evaluated for insomnia at a sleep-disorders clinic completed a sleep survey and underwent a semistructured clinical interview. As part of the evaluation, the specific precipitating events related to the onset of insomnia were identified. Subsequently, these factors were categorized (work-school, family, physical or psychological health, or indeterminate), and their affective valence (negative, positive, or indeterminate) was coded. The most common precipitating factors of insomnia were related to family, health, and work-school events. Sixty-five percent of precipitating events had a negative valence. These events differed with the age of onset of insomnia but not with the gender of participants. These findings are useful to identify potential risk factors for insomnia and improve our understanding of the natural history of insomnia.
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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".