Willingness to Pay for Quality of Life Technologies to Enhance Independent Functioning Among Baby Boomers and the Elderly Adults
Bibliographic record
Abstract
PURPOSE: We report the results of a study designed to assess whether and how much potential individual end users are willing to pay for Quality of Life Technologies (QoLTs) designed to enhance functioning and independence. DESIGN AND METHODS: We carried out a web survey of a nationally representative sample of U.S. baby boomers (aged 45-64; N = 416) and older adults (aged 65 and greater, N = 114). Respondents were first instructed to assume that they needed help with kitchen activities/personal care and that technology was available to help with things like meal preparation/dressing, and then they were asked the most they would be willing to pay each month out of pocket for these technologies. RESULTS: We modeled willingness to pay some (72% of respondents) versus none (28%), and the most people were willing to pay. Those willing to pay something were on average willing to pay a maximum of $40.30 and $45.00 per month for kitchen and personal care technology assistance, respectively. Respondents concerned about privacy or who were currently using assistive technology were less willing to pay. Respondents with higher incomes, who were Hispanic, or who perceived a higher likelihood of needing help in the future were more willing to pay. IMPLICATIONS: Consumers' willingness to pay out of pocket for technologies to improve their well-being and independence is limited. In order to be widely adopted, QoLTs will have to be highly cost effective so that third party payers such as Medicare and private insurance companies are willing to pay for them.
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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 | 0.002 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".