P-67 Community palliative care response to support care home residents and staff during the COVID-19 pandemic
Bibliographic record
Abstract
Aim To review our response to support residents and staff in Bromley Care Homes during the COVID-19 pandemic (April 2020–March 2021), reflect on our learning, joint working with colleagues and identify good practice to inform future models of care. Methods We collated clinical activity data and key case reviews of care home patients referred to our service over the year. Contemporaneous notes from formal/informal reflections and debriefs (internal and joint with GPs/CCG/other professionals) were reviewed. Themes from feedback of care home staff and managers (ad hoc and formal focus groups) were included with personal reflections. Results 345 patients were referred from 32 care homes. The majority (45%) in Quarter 1 (first wave), 14% in both Q2/Q3 and 27% in Q4 (second wave). Median age 89(53-110) with 69% >85 years; two-thirds female. 80% had a non-malignant primary diagnosis. Just over half died within the year; median time referral-to-death 17(0-229) days, 81(23%) remained on the caseload April 2021. Key themes in Q1 included: limited effectiveness of virtual assessments, atypical patient presentations, significant impact of social isolation on mental health/function, with families unable to advocate and inconsistent messaging about visiting rights. Care home staff were distressed, burnt out, feeling unsupported. In Q2/Q3 regular GSF meetings with care home-GPs, virtual teaching (webinars/ECHO) and staff ‘cascade project’ study days helped consolidate learning. The second wave was heralded by an outbreak in extra-care housing; care home-GPs were self-isolating. We led urgent senior clinical review and response. In Q4, daily COVID-19 monitoring meetings were key (representation from CCG, Public Health, Pharmacy, CH-GPs and St Christopher’s). Over a month, successive outbreaks were identified and resources coordinated to ensure clear advance care plans, timely review, targeted multiprofessional support to care home staff. Learning from each setting informed changes to approach in the next, including: understanding culture, correcting/enhancing infection control procedures, improved shared decision making and addressing hydration. Conclusion Senior clinical leadership, cross-boundary flexible working and willingness to learn together were vital.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".