Commentary: COVID in care homes—challenges and dilemmas in healthcare delivery
Bibliographic record
Abstract
The COVID-19 pandemic has disproportionately affected care home residents internationally, with 19-72% of COVID-19 deaths occurring in care homes. COVID-19 presents atypically in care home residents and up to 56% of residents may test positive whilst pre-symptomatic. In this article, we provide a commentary on challenges and dilemmas identified in the response to COVID-19 for care homes and their residents. We highlight the low sensitivity of polymerase chain reaction testing and the difficulties this poses for blanket screening and isolation of residents. We discuss quarantine of residents and the potential harms associated with this. Personal protective equipment supply for care homes during the pandemic has been suboptimal and we suggest that better integration of procurement and supply is required. Advance care planning has been challenged by the pandemic and there is a need to for healthcare staff to provide support to care homes with this. Finally, we discuss measures to implement augmented care in care homes, including treatment with oxygen and subcutaneous fluids, and the frameworks which will be required if these are to be sustainable. All of these challenges must be met by healthcare, social care and government agencies if care home residents and staff are to be physically and psychologically supported during this time of crisis for care homes.
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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 | 0.012 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.058 | 0.048 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".