MétaCan
Menu
Back to cohort
Record W4415455511 · doi:10.3397/in_2025_1074429

Outdoor-to-indoor noise perception survey of Canadians aging-in-place - preliminary results

2025· article· en· W4415455511 on OpenAlexaffabout
Iara Batista da Cunha, Jennifer A. Veitch, Ashley Nixon, Kelsey Eakin, Hannah Villeneuve, Marianne F. Touchie, William O’Brien, Jeffrey Mahn, Sabrina Skoda

Bibliographic record

VenueNOISE-CON proceedings · 2025
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsCarleton UniversityUniversity of Toronto
Fundersnot available
KeywordsAnnoyanceNoise (video)PerceptionNoise exposureAircraft noiseTraffic noisePopulationEnvironmental noise

Abstract

fetched live from OpenAlex

Worldwide, the population of aging adults is growing and recent studies in Canada have shown that most older adults would prefer to remain in their own homes as they age. Since indoor environmental quality directly affects the health and well-being of a building occupant, healthy aging in place will depend on the quality of the indoor conditions. Outdoor noise transmitted indoors is not only a cause of annoyance but is also a cause of sleep disturbance and a risk factor for the development of health issues. This paper reports preliminary results concerning outdoor-to-indoor noise perception in the homes of older adults in Canada, based on an online survey completed by over 400 participants from across the country, with 266 valid responses related to sleep and noise. Data related to sleep and noise were investigated and preliminary results showed that hearing noise at specific periods had an effect on sleep. Also, correlations between sleep and noise annoyance ratings were found. These results, together with future field measurements and laboratory studies, may support the development of guidelines for building construction and retrofit that include sound insulation performance of façades, ensuring that aging adults are protected against detrimental effects of outdoor noise sources.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.029
GPT teacher head0.361
Teacher spread0.333 · 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.

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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueNOISE-CON proceedingsSame topicNoise Effects and ManagementFrench-language works237,207