The Digital Divide and Urban Older Adults
Bibliographic record
Abstract
Computers and the Internet offer older adults opportunities and resources for independent living. However, many urban older adults do not use computers. This study examined the demographic, health, and social activities of urban older adults to determine variables that might predict the use and nonuse of computers in this population. A secondary data analysis was performed using the 2001 Detroit City-Wide Needs Assessment of Older Adults (n = 1410) data set. Logistic regression was used to explore potential differences in predictor variables between computer users and nonusers. Overall, computer users were younger (27%), had a higher level of education, were more likely to be employed, had an annual income greater than $20,000, and were healthier and more active than nonusers. They also were more likely to have memberships in community organizations and do volunteer work. Preferred computer activities included conducting Internet searches, playing games, writing, and communicating with family members and friends. The results suggest significant differences in demographic and health-related characteristics between computer users and nonusers among urban older adults. Although about a quarter of participants in this study used computers, the Digital Divide continues to exist in urban settings for scores of others.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".