INNOVATING LONG-TERM CARE DURING COVID-19 ACROSS FOUR NATIONS: INNOVATIONS, BARRIERS, AND FACILITATORS
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has starkly highlighted the vulnerabilities of older adults in residential long-Term Care Homes (LTCHs), amplifying calls to “reimagine” the LTC sector. Active engagement and inclusion of older adults, their families, and staff in the development and implementation of LTC innovations have been recommended by the United Nations and the World Health Organization, but little is known about such engagement in innovations. This symposium presents a comprehensive scoping review aimed at understanding innovations implemented in LTCHs in four countries (Brazil, Canada, Switzerland, United States) since 2020, and the practices of inclusion within innovation processes within LTCHs. The first paper outlines the scoping review methodology and summarizes national-level results, including the number of studies per country and types of studies (e.g., observational, experimental). The second paper presents an analysis of interventions deployed across different countries. The third paper focuses on the types and goals of technology-based innovations implemented in LTCHs during COVID-19 and summarizes the extent to which perspectives of residents, staff, and family caregivers were considered regarding technology-based innovations. The final paper examines the barriers and facilitators of the innovation process during COVID-19. This international symposium contributes to the reimagining and transformation of LTCHs into more resilient, inclusive, and high-quality living environments.
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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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".