Soundscape in dementia care environment
Bibliographic record
Abstract
Noise is an important sensory stimulus in any environment, especially in unfamiliar settings. Noise is impossible to ignore, and any disturbing noise or constant sound can be agitating, disturbing, and confusing for people who cannot escape the environment. People with dementia may already feel disoriented, isolated, and confined inside care facilities; uncontrolled sound can add to their anxiety and distress. Soundscape refers to the human perception of the auditory environment in context; it relies not only on the subjective quality of sound by quantifying the sound level but also the objective quality of the auditory environment based on people’s perception. The aim of this study is to describe the soundscape of the Specialized Dementia Unit at the Toronto Rehabilitation Institute, through data collection and observation, and to evaluate the quality of soundscape. Results show that the overall sound level (dB) of the unit is higher than recommendations, and also, the observation study shows that higher sound level not necessarily results in negative atmosphere, such as chaos or agitation. The findings prove a need for further study on the relation between the sound level and the perception of sound in dementia care units, which can foster improvement in quality of life for residents and staff.
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".