Family Caregivers’ Acceptance of Using Artificial Intelligence-Enabled Technology in the Care of Older Adults
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».