[Insomnia and increased use of sleep medication among seniors: problems and alternative treatment].
Bibliographic record
Abstract
OBJECTIVE: To investigate the problem of insomnia and increased used of sleep medication among seniors and to look at an alternative form of treatment (cognitive-behavioural therapy [CBT]) that has been adapted specifically for this population. QUALITY OF EVIDENCE: MEDLINE and PsycINFO were searched from 1990 to 2005 using the key words insomnia, elderly (older adults), hypnotics (sleep medication), and cognitive behavior therapy. When discussing the efficacy of treatment, sources quoted offer level I evidence. Studies on the deleterious effects of hypnotics primarily offer level II evidence, so their findings must be interpreted with caution (some studies present conflicting results). MAIN MESSAGE: Insomnia in elderly people is associated with marked distress or deterioration in social or physical functioning. Hypnotics can be dangerous for elderly people because they raise the risk of adverse effects on cognitive function and the risk of drug-drug interactions. Treatment should be based on CBT alone or on a combination of CBT and appropriate pharmaceutical therapy. CONCLUSION: Cognitive-behavioural therapy adapted specifically to the problem of insomnia in seniors is one of the recommended options. The gains often include a notable decrease in use of sleep medication and in the emotional distress associated with insomnia.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".