Smart home technology and the needs of the aging population in Southern Ontario
Bibliographic record
Abstract
The care-needs of the aging population of Southern Ontario, in addition to the support-needs of formal and informal caregivers, is ever-changing. The implementation of Smart Home Technology has been successful throughout Europe. While such research is lacking in Southern Ontario, the need for support is evidently growing. Smart Home Technology is defined as any type of technology that assists older adults to live independent, safe lives, by promoting health and wellbeing among users. Little research has attempted to understand the technology needs of the aging population, and none have focussed on the technology needs in long-term care, nor have taken the knowledge of front-line staff into consideration. Therefore, this qualitative study seeks to understand smart home technology needs in a long-term care home in Southern Ontario. This inquiry is based upon the opinions of Personal Support Workers (PSWs), nurses, and therapeutic recreationists. Data collection was pursued through open-ended face-to-face interviews (N=10). Data was transcribed, coded, and thematically analyzed into three major themes: existing technology; needed technology; and the realities of care workers’ daily work and tasks. Essentially, these findings have the ability to add to smart home technology literature and research, and provides a needs assessment for a typical long-term care home in Southern Ontario.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".