<p>Evaluation of the Effects of an Intervention Intended to Optimize the Sleep Environment Among the Elderly: An Exploratory Study</p>
Bibliographic record
Abstract
PURPOSE: The objective of this exploratory study was to evaluate the effects of a brief intervention intended to optimize the sleep environment in older people living in the community and to examine the way these effects change over time. METHODS: The sample was made up of 44 participants (19 men and 25 women) aged 65-85 years, with a mean age of 71.4. The intervention consisted in a group training session that covered the reasons for and ways to ("why" and "how") optimize a sleep environment. It comprises six themes: air quality and odors, luminosity, noises and sounds, comfort of the mattress, comfort of the pillow, and temperature. Participants completed a set of questionnaires before the intervention, and one month and four months later. RESULTS: Four months after the intervention, the replies to the questionnaires showed that the participants experienced reduced severity of insomnia, sleep latency and anxiety. The subjective quality of the participants' sleep along with their sleep efficacy also increased significantly during the same period. CONCLUSION: A brief intervention intended to optimize the sleep environment appears promising as an addition or alternative to the two other sleep improvement options generally offered to older people: medication and cognitive behavioral therapy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".