NEW COMMUNICATION TECHNOLOGIES FOR ENGAGING OLDER PATIENTS, FAMILIES, AND CAREGIVERS IN HEALTHCARE
Bibliographic record
Abstract
New communication technologies—such as the Internet, social media applications, mobile and “smart” phones—can facilitate patients, families and caregivers’ access and use of health information and navigation through the healthcare system. This scoping review mapped the literature on new communication technologies to engage older adults, their caregivers, and families in the healthcare system. The review identified types of technologies, how they are used, outcomes, strengths, weaknesses, and challenges. Peer-reviewed and grey literature was searched for empirical studies published between January 2002 and December 2014. Three reviewers reviewed the abstracts for inclusion. Articles were included if they focused on older adults (55+ years of age) and involved a “new” communication technology that facilitated an engagement with the healthcare system. 69 articles were identified as appropriate for inclusion. Overall findings indicate that various new communication technologies (i.e. e-health records, email and smartphone apps) can be used to improve engagement with the healthcare system, even amongst frail older adults. Users’ concerns with using new communication technologies included design, usability, and lack of experience. There is a gap in the literature concerning social media applications and how technology might influence and/or improve the caregiving experience. These findings suggest that training is an important component for introducing technology use in older patients, caregivers and families. While new communication technologies are a viable option for improving engagement with healthcare systems, older adults’ particular health needs must be considered for effective uptake and usage of these technologies.
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 | 0.016 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".