Telemedicine for Older Adults in Primary Care Practice: Multi-Phase Study Results
Bibliographic record
Abstract
Context: The COVID-19 pandemic has substantially changed the delivery of primary care health services. Indeed, more clinic-based care has turned into virtual remote care. Thus, it is necessary to develop a comprehensive remote virtual care method. Telemedicine (TM) has the potential to improve the quality of primary health care and increase accessibility to the population. Some studies indicated that TM was an efficient way to deliver medical services, especially to older people. Nevertheless, its use may represent a challenge to older populations as they may have different needs from general population due to potential age-related changes in perceptual, motor and cognitive capacities. Objective: To explore older adults’ and family physicians’ experience with telemedicine (TM) use during the COVID-19 pandemic, and to identify potential facilitators and barriers of TM use. Design and Analysis: Multi-phase design. Phase 1: Systematic review; Phase 2: Qualitative descriptive study; Phase 3: Integration of the results from Phase 1 and 2 using deliberative dialogue. Individual semi-structured interviews and focus groups findings were analyzed using inductive thematic analysis, based on the Consolidated Framework for Implementation Research. Setting: Montreal. Population studied: Individual semi-structured interviews with twenty-nine older adults from four McGill family medicine sites – Herzl, CLSC-CDN, Parc-Extension and Metro. Preliminary interviews findings informed focus groups with family physicians. Findings: Participants indicated that telemedicine contributed to maintain continuity of care and was convenient to resolve minor health issues, triage/preliminary consultations, and follow-up with their family physicians. TM was also considered particularly beneficial for persons with limited mobility; and reduced the exposure of older adults to potential high-risk environments. Nevertheless, participants expressed some concerns about the lack of visual contact, negatively impacting the physician-patient relationship and causing important details to be overlooked. Similarly, miscommunication difficulties may emerge due to language or hearing barriers. Family physicians perceived that most patients did not consider phone consultations as a medical act. Conclusion: Older adults consider TM to be a good alternative for accessing health-care services, when provided in a hybrid approach combined with inperson consultations with their family physician.
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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 | 0.039 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".