Nursing Homes and Long Term Care After COVID-19: A New ERA?
Bibliographic record
Abstract
Anna, 92 years old, former housekeeper, with a history of disabling stroke and disabling knee osteoarthritis, had been living in a Nursing Home (NH) for the last 6 years.In Anna's NH, 60% of the staff were affected by COVID-19.In March 2020, she was transferred to hospital due to hyperactive delirium where she was diagnosed with COVID-19 but then transferred to an alternative NH, designated for COVID-19 care.She died 2 days later having received palliative care but in unfamiliar surroundings.From the time her original NH was closed to visitors, until her death, she did not see any family members.Amanda, 87 years old, a widow and former schoolteacher, was diagnosed with Alzheimer's disease 8 years ago and moved to a nursing home 2 years ago.Prior to the outbreak she was able to follow a basic conversation and had limited mobility with a walker, participated in some group activities and had regular family visitors.She died after one month isolated in her room, initially because she had been in contact with another resident diagnosed with COVID-19 and later because of her deterioration.She did not experience the hallmark symptoms of COVID-19, and had a negative PCR for SARS-COV-2, but she became progressively more disoriented and spent more time in bed.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.039 | 0.030 |
| Insufficient payload (model declined to judge) | 0.018 | 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".