Preventing the Transmission of COVID-19 in Older Adults Aged 60 Years and Above Living in Long-Term Care: Rapid Review Update
Bibliographic record
Abstract
Objectives: The objective of this study was to examine the effect of measures of control and management of COVID-19, Middle East Respiratory Syndrome (MERS), and severe acute respiratory syndrome (SARS) in adults 60 years or above living in long-term care facilities. This is an update of previous work done by Rios et al. Methods: A rapid review was conducted in accordance with the Rapid Review Guide for Health Policy and Systems Research. Literature search of databases MEDLINE, Cochrane library, and pre-print servers (biorxiv/medrxiv) was conducted from July 31, 2020 to October 9, 2020. EMBASE was searched from July 31, 2020 until October 18, 2020. Titles and abstracts from public archives were identified for screening using Gordon V. Cormack and Maura R. Grossmans Continuous Active Learning (CAL) tool, which uses supervised machine learning. Results: Five observational studies and one clinical practice guideline were identified. Infection prevention measures identified in this rapid review included: social distancing and isolation, personal protective equipment (PPE) use and hygiene practices, screening, training and staffing policies. The use of PPE, laboratory screening tests, sick pay to staff, self-confinement of staff within the LTCFs for 7 or more days, maintaining maximum resident occupancy, training and social distancing significantly reduced the prevalence of COVID-19 infection among residents and/or staff of LTCFs (p<0.05). Practices such as hiring of temporary staff, not assigning staff to care separately for infected and uninfected residents, inability to isolate sick residents and infrequent cleaning of communal areas significantly increased the prevalence of infection among residents and/or staff of LTCFs (p<0.05). Conclusion: The available studies are limited to only three countries despite the global nature of the disease. The majority of these studies showed that infection control measures such as favourable staffing policies, training, screening, social distancing, isolation and use of PPE significantly improved residents and staff related outcomes. More studies exploring the effects infection prevention and control practices in long term care facilities are required.
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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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".