The Digital Divide: Examining the Use and Access to E-Health Based Technologies by Millennials and Older Adults
Bibliographic record
Abstract
Objectives: The digital divide is a complex phenomenon in which a metaphorical gap is present in between two groups of individuals who utilize ICT"s (information communication technologies).This gap provides cause for concern, especially with a society that is so technologically advanced in todays" day.Currently, little is known about how older adults and millennials access and use e-health based technologies.Hence, a systematic review was undertaken to address this noted gap in the literature.Methods: A systematic review of the literature was undertaken employing the following three databases (i) PubMed, (ii) ERIC, and (iii) CINAHL were examined using the search term "digital divide and generations" to identify potential articles were present.A data abstraction tool was created to obtain the following information: (i) author, (ii) year of publication, (iii) sample size, (iv) country of origin, (v) design/methods, (vi) major findings/outcomes obtained.Inclusion criteria included publication dates between the years of Jan 2009 to Aug 2018, written in the English language, targeting the target population of older adults aged 65+ and millennials, as well as being peer reviewed quantitative articles.Results/Conclusion: There is a dearth of literature in this topic, as well as a decline of research produced from Canada.The consequences and benefits of technology being integrated into daily living are just being investigated.Additionally, a change in the way that healthcare is currently used, received and distributed would also help attribute to the change to ensure that no generation is left behind in a technologically advanced society.
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 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 | 0.017 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".