TECHNOLOGY-BASED INNOVATIONS IN LONG-TERM CARE HOMES DURING COVID-19 AND RELATED INCLUSION PRACTICES
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has magnified challenges in Long-Term Care Homes (LTCHs), driving the adoption of technology-based solutions. The scoping review to explore technology-based innovations into a LTCH during COVID-19 in Canada, the US, Brazil and Switzerland produced 60 studies. The most reported technologies were use of telemedicine/virtual medical care tools that allowed for remote delivery of health care (n=16) within LTCHs, and robotics for clinical tasks and social interaction (n=14). Less frequently reported technologies were: wearables for health monitoring (n=2), mobile apps to improve residents’ mental well-being (n=2), and call light systems (n=1) between residents and staff. Most technologies were introduced by researchers (n=38) which primarily engaged with staff (n=30) and residents (n=24). Studies with telemedicine and robotics (n=30) focused on exploring the feasibility and acceptability of technology with the aim to improve optimal resident care and increase social connectedness. Notably, the most common goals of the technology-based innovations were to provide virtual medical care and to engage residents socially. The majority of the papers had active engagement (n=24) which relies on stakeholder’s actions (i.e. video calls). We also examined the level of inclusion and engagement of residents, their families, and staff and found varying levels of engagement. the term “co-design” was explicitly used (n=7) where the innovation was designed together with the stakeholders (for example, stakeholder opinions were considered during the process). This paper describes the engagement of stakeholders of these technological innovations, and points to the need for future research in this area.
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 | 0.028 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".