Perceived technology usefulness for caregiving among unpaid caregivers: a National Cross-Sectional Study
Bibliographic record
Abstract
Background Technological advancements have the potential to improve caregiving quality and alleviate caregiver burden by providing tools for real-time communication, monitoring, and care coordination. To assist with technology adoption among the 53 million unpaid caregivers nationwide, efforts are needed to better understand caregivers’ perceptions about the usefulness of certain technologies for caregiving. Methods Data were analyzed from a national sample of 483 unpaid caregivers using an internet-delivered questionnaire. All unpaid caregivers were eligible if they provided at least 8 h of weekly care for a care recipient aged 50 years or older. The primary dependent variable was the Perceived Technology Usefulness for Caregiving (PTUC) Scale, which is a composite score of six items ranging from 0 to 100. PTUC item responses were summed and averaged, and the overall PTUC scores were transformed into statistical tertiles (higher scores indicating more perceived technology usefulness for caregiving). An ordinal regression model was fitted to identify factors associated with higher PTUC tertiles. Results Across tertiles, unpaid caregivers who were younger (Beta = −0.018, p = 0.030) and male (Beta = 0.422, p = 0.048) reported higher PTUC Scale scores. Compared to non-Hispanic white caregivers, Hispanic/Latino (Beta = 0.779, p = 0.010), African American (Beta = 1.064, p < 0.001), and Asian (Beta = 0.958, p = 0.010) caregivers reported higher PTUC Scale scores. Unpaid caregivers with lower financial insecurity (Beta = −0.010, p = 0.003), higher caregiver strain (Beta = 0.149, p < 0.001), and more satisfaction with the support they receive for caregiving (Beta = 0.009, p = 0.002) reported higher PTUC Scale scores. Unpaid caregivers whose care recipients had less cognitive impairment reported higher PTUC Scale scores (Beta = −0.245, p = 0.048). Conclusion Findings indicate caregiver characteristics, caregiving dynamics, and available resources (financial and caregiving support) are associated with perceptions about the usefulness of technology for caregiving. The utility of technology for caregiving may be higher among unpaid caregivers with more caregiver strain or positive experiences with caregiving support.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".