BARRIERS AND FACILITATORS OF THE INNOVATION PROCESS IN LONG-TERM CARE HOMES DURING COVID-19
Bibliographic record
Abstract
Abstract The innovation process is complex and multidimensional regardless of the context in which it is applied. However, innovation sustainability can be even more challenging when applied in the Long-Term Care (LTC) sector. So, understanding the barriers and facilitators associated with innovation adoption can be essential to innovation sustainability in LTC homes. In the qualitative studies incorporated within the scoping review (n =18), an in-depth analysis was conducted to discern barriers and facilitators related to innovation in LTC homes during COVID-19. Barriers to innovation identified were related to residents (e.g., safety, privacy, and appropriateness), staff (e.g., workload and tight routine), LTC homes (e.g., infrastructure and material/human resources), and innovation itself (e.g., cost, technical and connectivity problems). Concerns about the inclusiveness of the residents, especially those with some health-related issues (e.g., dementia and macular degeneration) were also pointed out. Conversely, innovations are likelier to be adopted if they prove to be useful and convenient, align with the needs of both LTC homes and residents, and exhibit user-friendly features. From an organizational standpoint, facilitating innovation during COVID-19 involves preparing the LTC home, providing necessary equipment, and offering training and support to staff. Familiar members/caregivers also point as a positive factor the ability of innovation in relief of care burden. Codesign approaches, involving different stakeholders, might be essential to identifying barriers and facilitators and, therefore, contribute to the sustainability of the innovations inside LTC homes.
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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 | 0.068 | 0.199 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".