Investigating the multifaceted role of warm experts in enhancing and hindering older adults’ digital skills in Finland
Bibliographic record
Abstract
Despite widespread digitalization, certain marginal and societal groups can still encounter challenges in the digital world. Promoting digital inclusion and digital support aims to reduce these disparities and enable equal participation. In this article, we examine the quality and dynamics of informal learning and digital support provided by warm experts from the perspective of older adults' subjective experiences. Specifically, we ask ( Based on the first question, we further examine (2) how do the identified key elements of informal learning influence older adults' subjective learning experiences and independent use of digital technologies? Our inductive thematic analysis is based on participant-induced elicitation (PIE) interviews (n = 21), conducted with older adults (aged 65+) in Finland in 2018. Our findings suggest that older adults are a heterogeneous group with different support needs and diverse prior experiences with digital technologies. Even when digital support from friends and family is available, it does not always facilitate independent use or meaningful learning experiences. The results show how informal digital support provided by warm experts can not only enhance, but also hinder digital inclusion and independent aging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".