Older Adults and the Digital Divide in Romania: Implications for the COVID‐19 Pandemic
Bibliographic record
Abstract
Abstract At the beginning of the COVID‐19 pandemic, Romania invoked the 15th Amendment of the European Convention of Human Rights for emergency situations and issued an Emergency State Presidential Decree (first put into effect on March 16 and extended until May 15, 2020). This amendment allowed for exemptions from broad categories of human rights (e.g., the right to privacy and intimacy). Older people became the main target of the Romanian government's plans for isolation. Using data from the Romanian National Institute of Statistics and data gathered prior to the COVID‐19 pandemic from a longitudinal study on communication technologies used by older people in Romania (Loos, Nimrod, & Fernández‐Ardèvol, 2018; 2020), we examine the digital inequalities faced by Romanian elders. The current study addresses the following specific research questions: (1) what digital opportunities and limitations were faced by the older population of Romania: e.g., online shopping, asking for help from various organizations using online platforms, using online medical assistance, etc., and (2) how did digital inequalities shape the lives of older people in Romania? Although the data do not reflect the situation of older Internet users during pandemics, we used the most recent and detailed data regarding Internet behavior of older adults in Romania. The current article discusses the opportunities faced by those who already had access and Internet skills when the pandemic started, and the limitations faced by older people who were less digitally skilled. With many of the daily activities moving online during this period, older adults with poor digital skills or no Internet access risked social isolation. Also, we consider policy recommendations to reduce digital inequalities that affect elders. Although we focus on Romania, the current study typifies vulnerabilities older people face in emergent economies during the COVID‐19 pandemic.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".