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Record W3102644255 · doi:10.2147/cia.s277252

<p>Evaluation of the Effects of an Intervention Intended to Optimize the Sleep Environment Among the Elderly: An Exploratory Study</p>

2020· article· en· W3102644255 on OpenAlexaff
Sophie Desjardins, Sylvie Lapierre, Helen‐Maria Vasiliadis, Carol Hudon

Bibliographic record

VenueClinical Interventions in Aging · 2020
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsUniversité LavalUniversité de SherbrookeUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsMedicineIntervention (counseling)AnxietySleep onset latencySleep (system call)InsomniaCognitionPhysical therapySleep disorderClinical psychologyGerontologyPsychiatry

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.728
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.093
GPT teacher head0.408
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2020
Admission routes1
Has abstractyes

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