Diagnosis and management of geriatric insomnia: A guide for nurse practitioners
Bibliographic record
Abstract
PURPOSE: To discuss the assessment, diagnosis, and management of geriatric insomnia, a challenging clinical condition of older adults frequently seen by primary care providers. DATA SOURCES: Extensive literature review of the published research articles and textbooks. CONCLUSIONS: Complaints of insomnia among older adults are frequently ignored, considered a part of the normal aging process or viewed as a difficult to treat condition. Geriatric insomnia remains a challenge for primary care providers because of the lack of evidence-based clinical guidelines and limited treatment options available. Effective management of this condition is necessary for improved quality of life, which is a primary issue for the elderly and their families. Therefore, geriatric insomnia warrants thorough attention from the nurse practitioners (NPs) who provide care for older adults. IMPLICATIONS FOR PRACTICE: Undiagnosed or under treated insomnia can cause increased risk for falls, motor vehicle accidents, depression, and shorter survival. Insomniacs double their risk for cardiovascular disease, stroke, cancer, and suicide compared to their counterparts. Insomnia is also associated with increased healthcare utilization and institutionalization. NPs could play a central role in reducing the negative consequences of insomnia through a systematic approach for diagnosis, evaluation, and management.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".