Investigating Technology as a Possible Bridge to Age-in-Place
Bibliographic record
Abstract
Background. Technology can be a bridge to support the strategy of aging-in-place and enable older adults to remain at home and live more independently. Purpose: To investigate the impact of occupational therapist-led smart home technology educational sessions for older adults. Method: A concurrent embedded mixed methods design was used, wherein a descriptive qualitative sub-study was embedded within the predominant quasi-experimental quantitative design. Technology use, independence, social isolation, and experiences were documented. Findings: Thirty-nine older adults participated in the learning program and 14 participants completed semi-structured interviews. Most participants discussed being interested in purchasing technology in the future. Of those that purchased technology, they predominantly felt the technology helped them to feel safer in their homes and regarding their health. All participants stated that they learned about technologies they did not know were available and that the course was helpful. Conclusion: Educating older adults about the benefits and uses of smart home products contributed to the purchase or intent to purchase these products among most participants. The ability for this technology to address home safety and health monitoring is important for health providers and home modification experts to keep in mind while informing policymakers supporting aging-in-place.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".