INNOVATIONS IN LONG-TERM CARE SECTOR DURING COVID-19: A SCOPING REVIEW
Bibliographic record
Abstract
Abstract Different innovations were implemented in Long-Term Care (LTC) homes during COVID-19. Although designed to support stakeholders, innovations also have the potential to exacerbate disparities and inequalities in this sector. This scoping review aims to analyze innovations implemented in the residential LTC sector during the pandemic in four countries: Canada, USA, Brazil, and Switzerland. Potential studies were searched in six databases. Search strategy and eligibility criteria followed the mnemonic “PCC” (i.e., Population: residents, family members/caregivers, and other stakeholders, Concept: innovation, and Context: residential LTC sector). The studies retrieved from databases were screened by two independent reviewers. Discordances between reviewers were solved by consensus or by a third reviewer. A customized spreadsheet was used for data extraction. The search identified 4,056 registers. After excluding duplicate studies, 3,122 records were screened. From them, 98 studies fulfilled the eligibility criteria and were included. Half of the studies were conducted in the USA (51.0%), followed by Canada (39.8%). Few studies were conducted in Switzerland (n=3) and Brazil (n=1). Included studies have different design methods qualitative (18.4%), observational (17.3%), experimental (17.3%) and mixed methods (10.2%). Other types of design methods (opinion, commentary, experience reports, editorial, etc.) and literature reviews accounted for 28.6% and 8.2%, respectively. The innovations presented in the studies were classified according to type, level, and setting. Stakeholder engagement with innovation was categorized as active, passive, and co-designed. results of studies that involved stakeholders are presented in the following papers.
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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 | 0.023 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.018 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| 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".