Recruitment and Retention Challenges in a Technology-Based Study with Older Adults Discharged from a Geriatric Rehabilitation Unit
Bibliographic record
Abstract
PURPOSE: Technology has the potential to offer support to older adults after being discharged from geriatric rehabilitation. This article highlights recruitment and retention challenges in a study examining an interactive voice response telephone system designed to monitor and support older adults and their informal caregivers following discharge from a geriatric rehabilitation unit. METHODS: A prospective longitudinal study was planned to examine the feasibility of an interactive voice telephone system in facilitating the transition from rehabilitation to home for older adults and their family caregivers. Patient participants were required to make daily calls into the system. Using standardized instruments, data was to be collected at baseline and during home visits. FINDINGS: Older adults and their caregivers may not be willing to learn how to use new technology at the time of hospital discharge. Poor recruitment and retention rates prevented analysis of findings. CONCLUSIONS AND CLINICAL RELEVANCY: The importance of recruitment and retention in any study should never be underestimated. Target users of any intervention need to be included in both the design of the intervention and the study examining its benefit. Identifying the issues associated with introducing technology with a group of older rehabilitation patients should assist others who are interested in exploring the role of technology in facilitating hospital discharge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".