Sex and gender differences in technology needs and preferences among informal caregivers of persons with dementia
Notice bibliographique
Résumé
BACKGROUND: Dementia is a major public health concern associated with significant caregiver demands and there are technologies available to assist with caregiving. However, there is a paucity of information on caregiver needs and preferences for these technologies, particularly from a sex and gender perspective. To address this gap in research, the objectives of this study are to examine (1) the knowledge of technology, (2) perceived usefulness of technology, (3) feature preferences when installing and using technology and (4) sex and gender influences on technology needs and preferences among family caregivers of persons with dementia (PWD) across North America. METHODS: A secondary analysis was conducted on an existing cross-sectional survey with family caregivers of PWDs. Respondents were recruited through the Alzheimer Society of Canada, the Victorian Order of Nurses and Adult Day Programs and other Canadian health care provision institutes. Descriptive statistics, bivariate and multivariate analyses were used to describe the study sample, uncover differences between male and female caregivers and examine sex and gender influences on caregivers' technology needs and preferences. RESULTS: A total of 381 eligible responses were received over a nine month data collection period. The majority of respondents did not know much about and never used any technologies to assist with caregiving. "Being easy to install", "easy to learn how to use" and "cost" were identified as the most important features when purchasing and setting up technology, while "reliability" was identified as the most important feature when using technology. Most respondents were willing to pay up to $500 to acquire individual technologies. Controlling for other socio-demographic variables, female respondents were more likely to have some or more knowledge about technology for caregiving while male respondents were more willing to pay higher amounts for these technologies compared to their female counterparts. CONCLUSIONS: As one of the first studies of its kind, our findings represent a step towards the incorporation of sex and gender considerations such as cost and reliability in technology design and promotion for caregivers. Future efforts are warranted to establish an in-depth understanding of sex and gender influences in relation to other social and environmental factors.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, pas un consensus.
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