Preventing the transmission of COVID-19 and other coronaviruses in older adults aged 60 years and above living in long-term care: a rapid review
Bibliographic record
Abstract
BACKGROUND: The objective of this review was to examine the current guidelines for infection prevention and control (IPAC) of coronavirus disease-19 (COVID-19) or other coronaviruses in adults 60 years or older living in long-term care facilities (LTCF). METHODS: EMBASE, MEDLINE, Cochrane library, pre-print servers, clinical trial registries, and relevant grey literature sources were searched until July 31, 2020, using database searching and an automated method called Continuous Active Learning® (CAL®). All search results were processed using CAL® to identify the most likely relevant citations that were then screened by a single human reviewer. Full-text screening, data abstraction, and quality appraisal were completed by a single reviewer and verified by a second. RESULTS: Nine clinical practice guidelines (CPGs) were included. The most common recommendation in the CPGs was establishing surveillance and monitoring systems followed by mandating the use of PPE; physically distancing or cohorting residents; environmental cleaning and disinfection; promoting hand and respiratory hygiene among residents, staff, and visitors; and providing sick leave compensation for staff. CONCLUSIONS: Current evidence suggests robust surveillance and monitoring along with support for IPAC initiatives are key to preventing the spread of COVID-19 in LTCF. However, there are significant gaps in the current recommendations especially with regard to the movement of staff between LTCF and their role as possible transmission vectors. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42020181993.
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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".