Communication and Information Strategies Implemented by Four Hospitals in Brazil, Canada, and France to Deal with COVID-19 Healthcare-Associated Infections
Why this work is in the frame
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Bibliographic record
Abstract
During the COVID-19 pandemic outbreak, COVID-19 healthcare-associated infections (HAI) and risk management became major challenges facing hospitals. Using evidence from a research project, this commentary presents: 1) various communication and information strategies implemented by four hospitals and their staff in Brazil, Canada and France to reduce the risks of COVID-19 HAIs, and how they were perceived by hospital staff; 2) the flaws in communication in the hospitals; and 3) a proposed agenda for research on and action to improve institutional communications for future pandemics. By analyzing "top-down" strategies at the organizational level and spontaneous strategies initiated by and between professionals, this study shows that during the first waves of the pandemic, reliable information and clear communication about guidelines and health protocols' changes can help alleviate fears among staff and avoid misapplication of protocols, thereby reducing infection risks. There was a lack of a "bottom-up" communication channel, while, when making decisions, it is crucial to listen to and fully take into account staff's voices, experiences, and feelings. More balanced communication between hospital administrators and staff could strengthen team cohesion and lead to better enforcement of protocols, which in turn will reduce the risk of contamination, alleviate the potential impacts on staff health, and improve the quality of care provided to patients.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it