Older Adults’ Internet Use Is Varied, Suggesting the Need for Targeted Rather Than Broadly Focused Outreach
Notice bibliographique
Résumé
A Review of: van Boekel, L.C., Peek, S. T., & Luijkx, K.G. (2017). Diversity in older adults’ use of the Internet: Identifying subgroups through latent class analysis. Journal of Medical Internet Research, 19(5:e180), 1-10. doi: 10.2196/jmir.6853 Abstract Objective – To determine the amount and types of variation in Internet use among older adults, and to test its relationship to social and health factors. Design – Representative longitudinal survey panel of households Setting – The Netherlands Subjects – A panel with 1,418 members who were over 65 years of age had answered the survey questionnaire that included Internet use questions, and who reported access to and use of the Internet. Methods – Using information about the Internet activities the respondents reported, the authors conducted latent class analysis and extracted a best-fitting model including four clusters of respondent Internet use types. The four groups were analyzed using descriptive statistics and compared using ANOVA and chi-square tests. Analysis and comparisons were conducted both between groups, and on the relationship of the groups with a range of social and health variables. Main Results – The four clusters identified included: 1) practical users using the Internet for practical purposes such as financial transactions; 2) social users using the Internet for activities such as social media and gaming; 3) minimizers, who spent the least time on the Internet and were the oldest group; and 4) maximizers, who used the Internet for the widest range of purposes, for the most time, and who were the youngest group. Once the clusters were delineated, social and health factors were examined (specifically social and emotional loneliness, psychological well-being, and two activities of daily living (ADL) measures). There were significant differences between groups, but the effect sizes were small. Practical users had higher psychological well-being, whereas minimizers had the lowest scores related to ADLs and overall health (however, they were also the oldest group). Conclusions – The establishment of four clusters of Internet use types demonstrates that older adults are not homogeneous in their Internet practices. However, there were no marked findings showing differences between the clusters in social and health-related variables (the minimizers reported lower health status, but they were also the oldest group). Nevertheless, the finding of Internet use heterogeneity is an important one for those who wish to connect with older adults through Internet-based programming. The different patterns evidenced in each cluster will require differing outreach strategies. It also highlights the need for ongoing longitudinal research, to determine whether those who are currently younger and more technologically savvy will age into similar patterns that these authors found, or whether a new set of older adult Internet use profiles will emerge as younger generations with more Internet experience and affinity become older.
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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 | 0,007 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,005 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».