Categorical and resource inequalities in self-reliant internet use and use-by-proxy among older adults in Slovenia during the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic had a considerable impact on how older adults engaged online, with many using the internet for the first time or relying on family, friends, and peers to perform an activity online on their behalf, a form of internet use known as use-by-proxy. Since we lack large-scale research that compares what factors influence self-reliant internet use and use-by-proxy in older adults during the pandemic, this study seeks to fill this gap. Drawing on resources and appropriation theory, we examine how categorical (e.g., age, gender, education) and resource inequalities (e.g., social, material) shape internet use among older adults as well as the availability and activation of use-by-proxy among older internet non-users. We conducted three binary logistic regression models to analyze survey data collected in 2021 during the fourth wave of pandemic public health measures in Slovenia from a sample of 701 older adults aged 65+. The results show that personal and positional categorical disparities among older adults were significantly associated with their internet use during the pandemic, whereas bridging social capital was the only social resource positively associated with internet use. Conversely, categorical inequalities played a less important role in the availability of use-by-proxy than social resources. In fact, apart from occupation, bonding and bridging social capital were the only positive correlates of availability of proxy users among older internet non-users. Surprisingly, neither type of social capital was linked with the activation of use-by-proxy, which was only associated with two positional categorical disparities: marital status and residential area. Our findings suggest that addressing age-related digital inequalities after the pandemic requires a diversified approach that considers the heterogeneity of categorical and resource inequalities shaping older adults' self-reliant internet use and use-by-proxy. • Two forms of internet use are studied with resources and appropriation theory. • Survey data among older adults aged 65+ collected during the COVID-19 pandemic. • Categorical and resource inequalities are related to self-reliant internet use. • Social resources determine availability of use-by-proxy among older non-users. • Activation of use-by-proxy is related only to marital status and residential area.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".