Understanding Telehealth Adoption among the Elderly: An Empirical Investigation
Bibliographic record
Abstract
The adoption of telemedicine among the elderly is vital due to their unique healthcare needs and growing engagement with technology. This study explores the factors influencing their adoption behaviors, identifying both facilitating and inhibiting elements. While previous research has examined these factors, few have empirically assessed the simultaneous influence of barriers and enablers using a sample of elderly individuals. Using behavioral reasoning theory (BRT), this research investigates telehealth adoption behaviors of the elderly in India. A conceptual model incorporates both “reasons for” and “reasons against” adopting telehealth, capturing the nuanced dynamics of adoption behaviors. Data from 375 elderly individuals were collected to validate the model through structural equation modeling. The findings reveal that openness to change significantly enhances attitudes towards telehealth and “reasons for” adoption, influencing behaviors. This research contributes to the healthcare ecosystem by improving the understanding of telehealth adoption among the elderly. It validates the impact of openness to change alongside reasons for and against adoption, refining the understanding of behavior. By addressing impediments and leveraging facilitators, this study suggests strategies to maximize telehealth usage among the elderly, particularly those who are isolated, improving their access to medical services.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".