INNOVATION CHARACTERISTICS AND STAKEHOLDER INCLUSION IN LTC HOMES DURING COVID-19
Bibliographic record
Abstract
Abstract The toll the COVID-19 pandemic has taken on the quality of life of those living and working in Long-Term Care (LTC) homes resulted in calls for innovation in the LTC sector. Results from the observational, experimental, qualitative and mixed-method studies (n=62) showed that 50% and 45.2% of the innovations were characterized as products and processes, respectively. Organizational innovations were reported in 4.8% of the studies. Regarding the settings, the majority (52.4%) of the studies were performed at the micro level, followed by meso (25.4%) and macro (20.6%) levels. The identified innovations were predominantly (35.5%) related to new tools for clinical care and staff education, such as online pain assessment training and the creation of virtual instruments for shared end-of-life care decisions, followed by telecommunications interventions (22.6%), generally aimed at improving connections between residents and their families. COVID-19 detection/prevention (11.3%) was the third most common innovation category in this scoping review. Participants of the studies included staff (71%), residents (56%), family members/caregivers (29%) and experts/researchers (11.3%). Only 9.7% of them included all stakeholders. The innovations implied active engagement (62.9%), where the interventions relied on stakeholder’s actions (e.g. video calls); passive engagement (22.6%), where no actions from stakeholders were needed (e.g. ambient monitoring systems) and co-designed interventions (14.5%), where participants contributed with all stages (planning, implementing, evaluating) of the innovation. Innovations in LTC homes during COVID-19 were mainly characterized as new products or processes aimed at the care of residents and staff training, which required the active involvement of stakeholders, mostly staff.
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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 | 0.069 | 0.178 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 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".