eHealth in Geriatric Rehabilitation: An International Survey of the Experiences and Needs of Healthcare Professionals
Bibliographic record
Abstract
While eHealth can help improve outcomes for older patients receiving geriatric rehabilitation, the implementation and integration of eHealth is often complex and time-consuming. To use eHealth effectively in geriatric rehabilitation, it is essential to understand the experiences and needs of healthcare professionals. In this international multicentre cross-sectional study, we used a web-based survey to explore the use, benefits, feasibility and usability of eHealth in geriatric rehabilitation settings, together with the needs of working healthcare professionals. Descriptive statistics were used to summarize quantitative findings. The survey was completed by 513 healthcare professionals from 16 countries. Over half had experience with eHealth, although very few (52 of 263 = 20%) integrated eHealth into daily practice. Important barriers to the use or implementation of eHealth included insufficient resources, lack of an organization-wide implementation strategy and lack of knowledge. Professionals felt that eHealth is more complex for patients than for themselves, and also expressed a need for reliable information concerning available eHealth interventions and their applications. While eHealth has clear benefits, important barriers hinder successful implementation and integration into healthcare. Tailored implementation strategies and reliable information on effective eHealth applications are needed to overcome these barriers.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".