Examination of New Environmental Control Applications
Bibliographic record
Abstract
The aim of this study was to examine the application of new Environmental Control Systems (ECSs) in the homes of users and caregivers. The research questions were: (1) Can new ECS applications improve the activities of daily living (ADL) of people with significant functional limitations who require personal assistance? (2) Can new ECS applications replace home services and lessen caregiver burden? To answer these questions, user satisfaction regarding ECS applications, impact on ADL, technical performance, and caregiver burden were examined. This collaborative investigation involving a local community health care center, a telephone monitoring service, an industrial partner, and a university research team used a case study approach. Five users with moderate cognitive problems or significant functional limitations who required personal assistance were chosen, along with their caregivers, for a 3-month in-home trial to test new ECS alternatives. The ECS in the study featured remote control functions (e.g., door lock release, outside intercom), specific verbal reminders (e.g., reminders to turn off stove elements), and automatic functions (e.g., night-lights in the bathroom and hallway). Information was collected in the users' homes with three standardized questionnaires and a company-designed questionnaire. The overall technical performance of the ECS was found to be in most cases moderately efficient. Participant satisfaction revealed that ECS alternatives needed improvement with respect to the service aspects such as follow-up services and repair/servicing. Caregiver burden was lessened for psychological aspects but not for physical tasks. Users seemed to have a positive perception of the impact of the ECS on many of their ADL. We learned six lessons from this 15-month case study, namely: (1) the use of remote control by people with moderate cognitive impairments was difficult; (2) verbal reminders were greatly appreciated; (3) the automatic ECS applications needed more adjustment; (4) reactions varied depending on the participant's perspective; (5) other assessment tools might have been better suited to mild cognitive problems; and (6) removal of a beneficial product at the conclusion of the evaluation phase raised ethical considerations.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".