Middle-Aged and Older Adults' Information and Communication Technology Access: A Realist Review
Bibliographic record
Abstract
There continues to be a gap in information and communication technologies (ICTs) access and use between younger cohorts that have grown up with the technology (generation X and younger) and the next previous cohort (baby boomers and older). This is more pronounced among cohorts born prior to the 1930s, which have low access and use rates. Birth cohort, education, and income interact to create differences in familiarity, skill, and personal preference such that older adults with more education and higher incomes are more likely to access and use ICTs.\nTraining and support is one strategy that has been identified as able to increase access and use of ICTs among middle-aged and older adults. However, training needs to be tailored, relevant, and ongoing. Community service organizations that provide training and support require infrastructure support to purchase computers and tablets every three years as new technology emerges. In addition, ongoing funding is required to provide necessary training and support. This could be connected with other home programs, as in-home services are preferable.\nNegative stereotypes associated with ageist perspectives of older adults need to be systematically challenged and dispelled through public service campaigns and in mass media. Representations of older adults as incapable of learning how to use ICTs serves to perpetuate the digital divide.\nUsable and accessible design can enhance use of ICTs as some adults experience physical challenges such as declines in vision and hearing, and increased arthritis in their hands. Applying principles of universal design, and creating products that are accessible, reliable, and functional for most people, including those with disabilities, can lead to a generation of products that meet the needs of older adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".