Door Decals for Wayfinding and Redirection: A Quality Improvement Project Involving the Use of Clinical Real-Time Location Systems for Evaluation of Environmental Design Changes
Bibliographic record
Abstract
Background and Objectives: Environmental design modifications are important non-pharmacological interventions for people with dementia in older adult residential care, but their effects are difficult to measure objectively. In this paper, we present the assessment of the impact of door decals installed on patient rooms, offices, and exit doors on patient movements as an example of the uses of location data in evaluating environmental design interventions. Research Design and Methods: We undertook a quality improvement project in an inpatient specialized dementia unit using de-identified data from a clinical location monitoring system from 79 individuals with dementia admitted over time to 15 patient rooms to measure patient movements. In the first phase, decals were applied to 1 office and 6 patient room doors, and doors with and without decals were compared. In the second phase, patient movements were compared before and after a decal was applied to the remaining exit, office, and 9 patient doors. Main outcomes of interest were the number of daily approaches to concealed doors and daily approaches and entrances to patients' own and others' rooms. Results: Using location data, we identified a significant reduction in the number of approaches to and dwell time at office doors and exits. No differences were found in patient movements in relation to their own or others' rooms in either phase, although patients assigned a room with a decal tended to approach others' rooms with decals less often than those with a plain door. Discussion and Implications: Door decals successfully reduced patient contact with staff-only doors and exits but did not have a large impact on patient movement with respect to wayfinding. Location tracking systems provide an important opportunity to evaluate the impact of design interventions in situ in specific older adult care contexts.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".