Sleep problems in anxious and depressive older adults
Bibliographic record
Abstract
PURPOSE: The objective of this study was to identify the sleep problems most often encountered by the elderly according to the presence or absence of anxiety and mood disorders. The aim was also to determine whether groups of anxious, depressive, and asymptomatic individuals differ in relation to sleep onset latency; awakenings at night or early in the morning; subjective quality of sleep; taking of sleep medication; and daytime sleepiness. METHODS: Structured interviews based on the DSM-IV-TR were administered to a sample of 2,759 seniors aged 65 years and older at the participants' home by health professionals. RESULTS: Awakening was found to be the most common disturbance. Increased sleep onset latency was the second most frequent sleep difficulty. Taking more than 30 minutes to fall asleep was associated with the likelihood of meeting the diagnostic criteria for an anxiety disorder, and even reduced the risk of meeting the diagnostic criteria for a mood disorder rather than an anxiety disorder. Awakenings were associated with the probability of suffering from an anxiety disorder or a mood disorder. Quality of sleep, as perceived by the elderly, was not found to be associated with the probability of suffering from a mental disorder. CONCLUSION: These findings should help to facilitate the practitioner's diagnosis and add further nuances to be considered when encountering symptoms of an anxious or depressive appearance. All of these data also add fuel to the ongoing debate about whether anxiety and depression are one or two distinct categories of disorders.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".