Barriers to Telemedicine Video Visits for Older Adults in Independent Living Facilities: Mixed Methods Cross-sectional Needs Assessment
Bibliographic record
Abstract
BACKGROUND: Despite the increasing availability of telemedicine video visits during the COVID-19 pandemic, older adults have greater challenges in getting care through telemedicine. OBJECTIVE: We aim to better understand the barriers to telemedicine in community-dwelling older adults to improve the access to and experience of virtual visits. METHODS: We conducted a mixed methods needs assessment of older adults at two independent living facilities (sites A and B) in Northern California between September 2020 and March 2021. Voluntary surveys were distributed. Semistructured interviews were then conducted with participants who provided contact information. Surveys ascertained participants' preferred devices as well as comfort level, support, and top barriers regarding telephonic and video visits. Qualitative analysis of transcribed interviews identified key themes. RESULTS: Survey respondents' (N=249) average age was 84.6 (SD 6.6) years, and 76.7% (n=191) of the participants were female. At site A, 88.9% (111/125) had a bachelor's degree or beyond, and 99.2% (124/125) listed English as their preferred language. At site B, 42.9% (51/119) had a bachelor's degree or beyond, and 13.4% (16/119) preferred English, while 73.1% (87/119) preferred Mandarin. Regarding video visits, 36.5% (91/249) of all participants felt comfortable connecting with their health care team through video visits. Regarding top barriers, participants at site A reported not knowing how to connect to the platform (30/125, 24%), not being familiar with the technology (28/125, 22.4%), and having difficulty hearing (19/125, 15.2%), whereas for site B, the top barriers were not being able to speak English well (65/119, 54.6%), lack of familiarity with technology and the internet (44/119, 36.9%), and lack of interest in seeing providers outside of the clinic (42/119, 35.3%). Three key themes emerged from the follow-up interviews (n=15): (1) the perceived limitations of video visits, (2) the overwhelming process of learning the technology for telemedicine, and (3) the desire for in-person or on-demand help with telemedicine. CONCLUSIONS: Substantial barriers exist for older adults in connecting with their health care team through telemedicine, particularly through video visits. The largest barriers include difficulty with technology or using the video visit platform, hearing difficulty, language barriers, and lack of desire to see providers virtually. Efforts to improve telemedicine access for older adults should take into account patient perspectives.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".