Elder People and Personal Data: New Challenges in Health Platformization
Bibliographic record
Abstract
In Uruguay, as in many countries around the world, healthcare providers are looking to digital technologies to enhance service provision. This includes introducing new data-intensive systems that facilitate connections between healthcare providers and patients and maintaining records of these interactions. This article considers the numeric ability of older citizens to critically assess the implications of platformization and datafication within the Uruguayan healthcare system with a view to identifying implications for digital literacy programs. The ability of older people to manage their personal data within healthcare systems shapes their ability to enact citizenship and human rights. This reality came into sharp relief during the recent Covid-19 pandemic, demonstrating the extent to which core social services have become datafied and digitally mediated, as well as their potential to deepen digital divides where senior citizens are concerned. Critical perspectives on technological change, well-being, and ageing offer useful perspectives on this challenge. Drawing inspiration from these perspectives, in this article, we explore the results of a digital literacy initiative that worked with 16 seniors to explore their experiences of personal data collection within Uruguay’s new National Comprehensive Health System. Our approach simultaneously worked to build digital literacy while also revealing the complex relationships and disconnections between the ontological frameworks mapped onto healthcare by systems designers and the reality of older people. In the conclusions, we consider the implications of these observations for seniors’ digital literacy interventions that foster seniors’ critical understanding of their data subjectivity in the context of local healthcare systems.
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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 | 0.030 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.002 | 0.004 |
| 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".