Barriers & Facilitators of Innovation in Long-Term Care Homes During COVID-19: Insights from Brazilian experts
Bibliographic record
Abstract
Abstract The COVID-19 pandemic profoundly disrupted global healthcare systems, with Long-Term Care Homes (LTCHs) facing unprecedented challenges that underscored the critical role of innovation in crisis response. This cross-sectional qualitative study, part of the CONNECTED consortium, examined insights from 12 Brazilian experts (researchers, LTCH managers, industry representatives, and heads of local/national LTCH organizations) to explore barriers and facilitators in implementing innovations during COVID-19. Data were collected through 12 semi-structured interviews (from March to October 2024) and analyzed using thematic analysis. The innovations implemented during COVID-19 were related to: 1) safety and preventive measures, 2) communication and social connections tools, 2) telehealth, 3) remote training programs for LTCH staff, and 4) national voluntary movement to support the LTCHs. Interviewees identified multiple systemic barriers to innovation implementation in LTCHs, including resource constraints (financial and human), digital illiteracy among residents, institutional resistance to change, inadequate staff training, regulatory hurdles, infrastructural limitations, and entrenched cultural stigma surrounding LTCH care. Conversely, experts highlighted key facilitators: targeted governmental and organizational funding, national-driven knowledge sharing, implementation of rapid communication channels, and cross-sector partnerships between public health authorities and LTCHs—all of which proved critical in mitigating pandemic-related disruptions. In Brazil, while the COVID-19 pandemic pressured catalyzed rapid adoption of vital innovations, from telehealth platforms to national solidarity initiatives, systemic barriers like resource scarcity, infrastructural gaps, and cultural stigma persisted as ingrained systematic issues. Therefore, addressing these systemic challenges is imperative to develop the resilience of the Brazilian long-term care sector and ensure preparedness for future health crises.
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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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 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".