eHealth in geriatric rehabilitation: an international consensus study
Bibliographic record
Abstract
PURPOSE: Current evidence on the use of eHealth in geriatric rehabilitation is limited. This aim of this study was to achieve international consensus on three key eHealth-related topics in geriatric rehabilitation: the use, domains, and scientific evaluation of eHealth. Additionally, we developed a model that provides insight into the use of eHealth in geriatric rehabilitation. METHODS: An international, two-round Delphi study was conducted. Two models served as a framework for the initial statement draft, with a total of 28 statements based on our systematic review results, an international survey, and expert opinion. Eligible healthcare professionals working in geriatric rehabilitation facilities were recruited across 10 countries. RESULTS: Eighty healthcare professionals participated in round one and 47 in round two. In the first round, consensus was obtained for 20 of the 28 statements (71%). Prior to round two, four statements were revised, two statements were combined, and one statement was removed. In round two, consensus was obtained on six statements, bringing the total to 26: three related to the use of eHealth, five to the domains of eHealth, and 18 related to the scientific evaluation of eHealth. CONCLUSION: International consensus has been reached on the use, domains, and scientific evaluation of eHealth in geriatric rehabilitation. This first step in generating reliable knowledge and understandable information will help promote a consistent approach to the development, implementation, and scientific evaluation of eHealth in geriatric rehabilitation.
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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 | 0.322 | 0.328 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| 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".