Barriers & Facilitators of Innovation in Long-Term Care Homes During COVID-19 in Brazil: views from key stakeholders
Bibliographic record
Abstract
Abstract The COVID-19 pandemic had a significant effect on individuals residing and working in long-term care homes (LTCHs). This scenario highlighted systemic disadvantages and vulnerabilities and called for innovations in the sector around the world. In Brazil, the LTC segment is historically marked by inequalities, underfunding, absence of systematically organized data and research. Therefore, little is known about the perception of key stakeholders about innovation in LTC. This cross-sectional qualitative study, part of the CONNECTED consortium, investigates potential barriers and facilitators in implementing innovations during COVID-19 from the perspective of key stakeholders from four LTCHs. The team conducted 15 semi-structured interviews (residents: n = 5, family members/caregivers: n = 5, staff: n = 5) from March to October 2024. Interviews were transcribed verbatim and the results were analyzed using thematic analysis through Rapid Assessment Procedure (RAP) sheets. Besides financial constraints and workforce shortage, barriers to innovation during COVID-19 included lack of involvement in decision-making for residents, family members, and direct care workers. Residents reported uncertainty in whether their opinions were considered. Communication issues were shared by all groups and influenced their ability to adapt to the new protocols. Facilitators included the willingness to change and the perceived gains of innovations, including enhanced safety measures and the use of technology, which was critical for staying connected. Staff emphasized the need for structured support, while families focused on emotional connections. Such insights underscore the need for inclusive, co-designed practices in Brazilian LTCHs in order to empower the sector and ensure the dignity of such populations.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".