Relational Digital Agency: An Everyday Life Study of Mobile Communication in Nursing Homes
Bibliographic record
Abstract
The pervasive association of long-term care with frailty and dependency has shaped research agendas. Everyday life studies that take into account care home residents' knowledge, values, and experiences are few and far between. This research engages care home residents in dialogue to co-produce understanding about their lived experiences with mobile technologies. Drawing on qualitative research with 39 care home residents at long-term care sites in Canada, the paper calls for reframing digital inequalities in terms of relational digital agency. The analysis describes how meaningful communication environments in long-term care involve a wide range of factors, including effective access to analogue media and wider support networks, which enable residents to put meaningful limits on their uses of mobile devices. Moreover, the findings show how having the opportunity to deny and contest mobile technologies can be an important part of feeling socially and digitally included, which brings question to existing measures of digital inclusion that focus on quantity and quality of technology use. Whereas most research on digital agency has concerned youth, this paper develops an understanding of relational digital agency to account for long-term care residents' experiences negotiating and adapting to digital change.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".