Preferences for technology versus human assistance and control over technology in the performance of kitchen and personal care tasks in baby boomers and older adults
Bibliographic record
Abstract
Purpose: Quality of Life technology (QoLT) stresses humans and technology as mutually dependent and aware, working together to improve task performance and quality of life. This study examines preferences for technology versus human assistance and control in the context of QoLT. Method: Data are from a nationally representative, cross-sectional web-based sample of 416 US baby boomers (45–64) and 114 older adults (65+) on preferences for technology versus human assistance and control in the performance of kitchen and personal care tasks. Multinomial logistic regression and ordinary least squares regression were used to determine predictors of these preferences. Results: Respondents were generally accepting of technology assistance but wanted to maintain control over its’ operation. Baby boomers were more likely to prefer technology than older adults, and those with fewer QoLT privacy concerns and who thought they were more likely to need future help were more likely to prefer technology over human assistance and more willing to relinquish control to technology. Conclusions: Results suggest the need for design of person- and context-aware QoLT systems that are responsive to user desires for level of control over operation of the technology. The predictors of these preferences suggest potentially receptive markets for the targeting of QoLT systems. Implications for RehabilitationIn anticipation of potential needs for help with instrumental activities of daily living (IADL) and activities of daily living (ADL), approximately half of US baby boomers and older adults are open to having intelligent, person-aware technology provide assistance. The remainder would prefer human assistance.US baby boomers and older adults are generally not in favor of relinquishing control to the technology when receiving IADL or ADL assistance.Preferences for technology versus human assistance and control over technology vary by socio-demographic, health-related, and technology attitude variables.Intelligent, person-aware quality of life technologies for rehabilitation must be designed to be sensitive to user characteristics and preferences for control over operation of the technology.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.011 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".