Virtual Care Uptake by Older Patients with Preventable Chronic Conditions: A Meta-Synthesis of Their Capability, Opportunity, and Motivation (Preprint)
Bibliographic record
Abstract
BACKGROUND Virtual care is constantly evolving, with increased uptake observed among older patients living with clinically complex conditions. Reviews are limited and exiting reviews did not explain the approaches and behaviors of older patients with preventable chronic conditions towards virtual care. OBJECTIVE To investigate the capability, opportunity, and motivation in virtual care uptake among older patients with preventable chronic conditions. METHODS A comprehensive search was conducted in MEDLINE EBSCO, PubMed, ProQuest, CINAHL, and Scopus, followed by the six-step meta-synthesis method to guide retrieval of data, analysis, and reporting. The Theoretical Domains Framework (TDF) and the Capability, Opportunity, Motivation, and Behavior (COM-B) Model provided a structure for reporting deductive themes and further interpretation of results. RESULTS The meta-synthesis included 21 studies undertaken in the United States (n=12), Australia (n=3), Canada (n=2), Europe (n=3), and Asia (n=1), encompassing a total of 1005 participants. Three key themes were identified: the psychological and physical capabilities of older patients with preventable chronic conditions, the influence of the social and environmental factors, and motivational drivers. Psychological capabilities and technological literacy were pivotal in enhancing health outcomes through virtual care, as they promoted accountability and proactive health management. Environmental factors, including social support networks and accessible health information, influence virtual care adoption and effectiveness, while patient motivation remains crucial for sustained engagement with digital health interventions. Studies showed that patients are generally amenable to the use of virtual care. Environmental and behavioral factors of older patients with chronic conditions influence their attitudes towards virtual care. CONCLUSIONS Studies showed that patients are generally amenable to the use of virtual care. Environmental and behavioral factors of older patients with chronic conditions influence their attitudes towards virtual care.
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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 | 0.046 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.026 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".