Bibliographic record
Abstract
Background The presenter will be discussing home monitoring–based telenursing for people with chronic conditions. This technology has been implemented in home-care individuals with chronic obstructive pulmonary disease, type 2 diabetes, congestive heart failure, lung cancer, and amyotrophic lateral sclerosis, who are treated at home, including during the COVID-19 pandemic. The pandemic, which began in late 2019, has limited our everyday activities and opportunities to connect with people. Older adults with chronic conditions are most especially affected. While telenursing practice is not so familiar in the Japanese context, the Japan Academy of Home Care (2021) first defined telenursing as “information and communication technologies involving telecommunication provided by nurses.” Furthermore, we are providing seminars for the nurses and expanding their capacity on how to provide efficient telenursing support for people receiving home care. Objective This study aims to present effective telenursing practice examples, as well as the challenges surrounding the use of technology in care for older adults with chronic conditions during and beyond the COVID-19 pandemic. Methods A fully longitudinal mixed methods design was used to evaluate the physical and emotional fluctuations of people from qualitative and quantitative strands, and we integrated the results and meta-inferences. Results The patients showed a continuous change over time in terms of their physical and psychological status. Living with symptoms, the patients were constantly reminded of the reality of their disease and the activity limitations the pandemic brought. At times, they were able to find hope for the future by actively controlling and managing their disease, maintaining their health and physical function, and realizing that they could live a normal life. On the other hand, they experienced a loss of activity, a decline in physical function, and anxiety about the future, brought about by the pandemic. Thus, people who receive telenursing are on a dynamic disease trajectory that vacillates between hope and despair, and telenursing can help them. Conclusions Performing telemonitoring and telerehabilitation of older adults throughout the pandemic, as well as adapting to their physical and emotional fluctuations, will improve their quality of life. Conflicts of Interest None declared.
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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 | 0.021 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".