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Family Caregivers' Acceptance of Using Artificial Intelligence-Enabled Technology in the Care of Older Adults /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Family Caregivers' Acceptance of Using Artificial Intelligence-Enabled Technology in the Care of Older Adults // Amanda Yee.
Author:	Yee, Amanda,
Description:	1 electronic resource (148 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-11.
Contained By:	Masters Abstracts International85-11.
Subject:	Womens health. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31086630
ISBN:	9798382616988

Family Caregivers' Acceptance of Using Artificial Intelligence-Enabled Technology in the Care of Older Adults /
Yee, Amanda,

Family Caregivers' Acceptance of Using Artificial Intelligence-Enabled Technology in the Care of Older Adults /Amanda Yee. - 1 electronic resource (148 pages)

Source: Masters Abstracts International, Volume: 85-11.

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 oneDiscussion 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.

English

ISBN: 9798382616988Subjects--Topical Terms:

3562508
Womens health.

Family Caregivers' Acceptance of Using Artificial Intelligence-Enabled Technology in the Care of Older Adults /
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500 $a Source: Masters Abstracts International, Volume: 85-11.
500 $a Advisors: Rahimi, Samira Abbasgholizadeh; Yaffe, Mark John.
502 $b M.S. $c McGill University (Canada) $d 2023.
520 $a 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 oneDiscussion 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.
520 $a Contexte:Les technologies fondees sur l'intelligence artificielle (AI) pourraient assister les proches aidants (FCGs) a fournir des soins aux personnes agees. La theorie unifiee de l'acceptation et de l'utilisation de la technologie (UTAUT) a ete developpee pour comprendre l'acceptation de la technologie, mais aucune etude appliquant cette theorie n'a ete effectue sur l'acceptation de l'AI parmi les proches aidants canadiens.Objectifs:Cette etude visait a examiner l'intention comportementale (BI) des FCGs quebecois d'age mur d'utiliser la technologie basee sur l'IA pour les soins aux personnes agees et a evaluer la capacite predictive des variables theoriquement candidates.Methodes:Cette etude transversale s'agissait d'un sondage en ligne utilisant un modele modifie de l'UTAUT et des echelles Likert pour mesurer la BI et neuf variables predictives : attente de performance, attente d'effort, influence sociale, conditions facilitantes, anxiete liee a la technologie, confiance percue, cout percu, confiance envers la source de conseils pour les soins (professionnel de la sante versus technologie basee sur l'IA) et confiance envers les conseils des professionnels de la sante pour l'utilisation de la technologie basee sur l'IA.Analyses:Des statistiques descriptives et une analyse par foret aleatoire (RF) ont ete utilisees. Pour determiner l'importance relative des variables dans la prediction de la BI, nous avons utilise le pourcentage d'augmentation d'erreur quadratique moyenne (MSE). Les valeurs predictives basees sur la RF ajustee ont ete utilisees pour examiner la direction des associations entre les variables et le BI. Des analyses supplementaires ont ete effectuees en transformant le pourcentage d'augmentation de l'MSE en une echelle de quatre points, qui a ete utilisee pour quantifier le changement dans le score BI predit du modele complet (c'est-a-dire les neuf variables) et du modele reduit (c'est-a-dire l'elimination d'une variable et la retention de huit variables).Resultats:Sur les 465 visiteurs uniques du sondage, 201 l'ont completee et parmi eux, 199 etaient eligibles (taux de reponse : 17 % et taux d'achevement : 43 %). En ce qui concerne l'utilisation future des technologies basees sur l'AI, 45 % des FCGs n'etaient pas certains et 37 % ne prevoyaient pas l'utiliser autant que possible. Toutefois, s'ils y avaient acces, les FCGs auraient davantage l'intention de s'en servir (62 %). La variance expliquee des RF varie de 56% a 83%. Six variables (attente de performance, attente d'effort, influence sociale, conditions facilitantes, confiance percue et confiance envers les conseils des professionnels de la sante pour l'utilisation de la technologie basee sur l'IA) ont demontre une association positive, deux variables (anxiete liee a la technologie et cout percu) ont demontre une association negative et une variable (confiance envers la source de conseils pour les soins (professionnel de la sante ou technologie basee sur l'IA)) a demontre une association quadratique approximative avec la BI. La variable predictive la plus importante etait l'influence sociale, avec une augmentation de 35 % de l'MSE. En comparant les modeles complets aux modeles reduits, la plupart des scores BI predits n'ont pas decales de plus de 0,12 unite sur l'echelle, ce qui suggere que la bonne performance du modele est due a la valeur explicative complementaire de tous les predicteurs plutot que d'un seul.Discussions et implications:Si accessible, les FCGs font preuve d'une plus grande BI pour utiliser les technologies fondees sur l'AI. Les analyses RF ont demontre que toutes les variables predictives jouaient un role complementaire dans la prediction de la BI des FCGs, soulignant la necessite des parties concernees du domaine de l'AI, du gouvernement et des soins de sante d'accorder de l'importance a ces variables.
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650 4 $a Social support. $2 lcstt $3 3267467
650 4 $a Revisions. $3 3681635
650 4 $a Caregivers. $3 580207
650 4 $a Public health. $3 534748
650 4 $a Social psychology. $3 520219
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710 2 $a McGill University (Canada). $e degree granting institution. $3 3759190
720 1 $a Rahimi, Samira Abbasgholizadeh $e degree supervisor.
720 1 $a Yaffe, Mark John $e degree supervisor.
773 0 $t Masters Abstracts International $g 85-11.
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792 $a 2023
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31086630