Bibliographic record
Abstract
This rapid communication highlights stroke telerehabilitation, a health care service that provides daily monitoring of the care of patients recovering from stroke, delivering convenient and immediate feedback for patients, family, and caregivers. The delivery, management, and coordination of nursing care services, provided via telecommunications technology, is a convenient method of delivering health care to patients recovering from stroke. It is important to assess the service quality of the telehealth process and to establish the role of telehealth nursing and related technologies in the care of patients recovering from stroke. Studies show that even though both health professionals and participants have reported high levels of satisfaction and acceptance of telerehabilitation interventions, the quality of the evidence on telerehabilitation in poststroke care remains low. Conducting a quality study of telehealth rehabilitation for patients recovering from stroke will help assess if home health agencies with telehealth capabilities caring for patients recovering from stroke and patients with chronic diseases can provide quality care to patients in their home and fill this health care gap. Patients that are severely handicapped and impaired and unable to reside in their home environment are not included in telerehabilitation services provided by the home care agency. It would be informative to study the benefits of telerehabilitation and the care provided to patients recovering from stroke within nursing homes, given the need for social distancing to reduce disease transmission during the current coronavirus disease (COVID-19) global health pandemic. Using telerehabilitation would mean that patients have a lower risk of exposure to infectious agents. Further research into telehealth interventions and stroke management in home care is crucial.
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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".