Family Caregivers’ Acceptance of Using Artificial Intelligence-Enabled Technology in the Care of Older Adults
Bibliographic record
Abstract
Background: Artificial intelligence (AI)-enabled technology might aid family caregivers (FCGs) in providing older adult care. The Unified Theory of Acceptance and Use of Technology (UTAUT) was developed to understand technology acceptance, but no studies applying it have focused on Canadian FCGs’ acceptance of AI.Aim: This study sought to examine middle-aged Quebec FCGs' behavioural intention (BI) to use AI-enabled technology for older adult care, and to assess the predictive capability of candidate predictor variables.Method: This was a cross-sectional online survey using an extended UTAUT model and five-point scales measured BI and nine predictor variables: performance expectancy, effort expectancy, social influence, facilitating conditions, technology anxiety, perceived trust, perceived cost, confidence in the source of advice for care (healthcare professional vs AI-enabled technology) and confidence in healthcare professionals’ advice for the use of AI-enabled technology.Analysis: Descriptive statistics and random forest (RF) analysis were used. To establish the variable’s relative importance in predicting BI we used the percent increase in mean-squared error (MSE). The predicted values based on the fitted RF were used to examine the direction of the associations between the variables and BI. Further analyses were conducted by transforming the percent increase in MSE to a four-point scale, which was used to quantify the change in predicted BI score from the full (i.e., all nine variables) to reduced models (i.e., removal of one variable and retention of eight).Results: Of 465 unique survey visitors, 201 completed it, and among them, 199 were eligible (response rate: 17% and completion rate: 43%). Regarding the future use of AI-enabled technologies, 45% of FCGs were uncertain, and 37% could not anticipate using it as much as possible. However, if it were accessible, the FCGs indicated greater intentions to use it (62%). The RFs’ variance explained ranged from 56% to 83%. Six variables (i.e., performance expectancy, effort expectancy, social influence, facilitating conditions, perceived trust, and confidence in healthcare professionals’ advice for the use of AI-enabled technology) showed a positive, two variables (i.e., technology anxiety and perceived cost) showed a negative, and one variable (i.e., confidence in the source of advice for care (healthcare professional vs AI-enabled technology)) showed an approximate quadratic association with BI. The most important variable predicting BI was social influence with a 35% increase in MSE. When comparing the full to reduced models, most predicted BI scores shifted no more than 0.12 units on the scale, suggesting that the good model performance was due to the complimentary explanatory value of all predictors rather than one.Discussion and Implications: If accessible, FCGs show greater BI to use AI-enabled technology. RF analyses indicated that all predictor variables had a complementary role in predicting FCGs’ BI, highlighting the need for AI, government, and healthcare stakeholders to address those variables
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".