Before the COVID-Vaccine—Vulnerable Elderly in Homecare
Bibliographic record
Abstract
BACKGROUND: At the beginning of 2020, the COVID-19 virus was spreading all over the world. Frail elderly were at risk for illness and death. Isolation seemed to be the best solution. The aim of this paper was to describe how the lockdown affected elderly homecare patients. METHODS: We used an international self-reported screening instrument built on well-documented risk factors adapted to COVID-19. We considered ethical, legal, and practical concerns. The research included telephone interviews with 30 homecare patients. RESULTS: Seventy percent lived alone. Seventy-three percent of the sample suffered from major comorbidity. Cardiovascular disorder was the most frequent diagnosis. Nineteen (63.3%) needed help for personal care. Several of the participants were lonely and depressed. The homecare teams struggled to give proper care. The health authorities encouraged the population to reduce their outside physical activities to a minimum. The restrictions due to COVID-19 affected daily life and several respondents expressed uncertainties about the future. CONCLUSIONS: It is important to describe the patients' experiences in a homecare setting at the initiation of lockdowns due to COVID-19. The isolation protected them from the virus, but they struggled with loneliness and the lack of physical contact with their loved ones. In the future, we need to understand and address the unmet needs of elderly homecare patients in lockdown.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".