Factors influencing long-term care facility performance during the COVID-19 pandemic: a scoping review
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic wreaked havoc on long-term care facilities (LTCFs). Some LTCFs performed better than others at slowing COVID-19 transmission. Emerging literature has mostly described infection prevention and control strategies implemented by LTCFs during the pandemic. However, there is a need for a comprehensive review of factors that influenced the performance of LTCFs in containing COVID-19 spread to inform public health policy. OBJECTIVE: To build on the existing literature, we conducted a scoping review of factors that influenced LTCF performance during the COVID-19 pandemic using a multidimensional conceptual framework of performance. METHODS: , 2021. Retrieved records were screened for context (COVID-19 pandemic), population (LTCFs), interest (internal and external factors that influenced LTCF performance), and outcomes (dimensions of performance: equity, accessibility, reactivity, safety, continuity, efficacy, viability, efficiency). Descriptive characteristics of included articles were summarized. Dimensions of performance as well as internal (e.g., facility characteristics) and external (e.g., visitors) factors identified to have influenced LTCF performance were presented. RESULTS: We retained 140 articles of which 68% were classified as research articles, 47% originated in North America, and most covered a period between March and July 2020. The most frequent dimensions of performance were "efficacy" (75.7%) and "safety" (75.7%). The most common internal factors were "organizational context" (72.9%) and "human resources" (62.1%), and the most common external factors were "visitors" (27.1%) and "public health guidelines" (25.7%). CONCLUSIONS: Our review contributes to a global interest in understanding the impact of the COVID-19 pandemic on vulnerable populations residing and working in LTCFs. Though a myriad of factors were reported, a lack of randomized controlled trials makes it impossible to establish causality between the identified factors and LTCF performance. The use of a multidimensional framework can be recommended to evaluate healthcare system performance not merely in terms of efficacy and safety, but alongside other critical dimensions such as efficiency and equity. TRIAL REGISTRATION: Research Registry ID: researchregistry7026.
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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 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".