Older Adults’ Internet Use Is Varied, Suggesting the Need for Targeted Rather Than Broadly Focused Outreach
Bibliographic record
Abstract
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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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".