Home-based nursing care: what do we know about multidrug-resistant organisms in this setting?
Bibliographic record
Abstract
Abstract Background The burden and complexity of home-based nursing care have intensified due to earlier discharges from hospitals and patients living longer at home with increasing co-morbidities. This growing population of vulnerable patients, combined with the worldwide increase in the prevalence of multidrug-resistant organisms (MDROs), poses a new burden on home-based nursing staff. Home-based nursing care differs significantly from hospitals and nursing homes, challenging implementation of infection prevention and control (IPC) measures. It is therefore important to gain insight into the attitudes, experiences and needs of home-based nursing staff. Also, to better understand the scope of MDRO carriage and transmission in this setting, including its challenges. Methods Cross-sectional studies were conducted in the Netherlands including a survey and focusgroup study among home-based nursing staff, supplemented by a report of cases of MDRO clusters. Results We have identified several factors adding to the complexity of responding to an MDRO outbreak with involvement of home-based nursing care. First, inapplicable recommendations in guidelines/protocols or from infection preventionists, often aimed at intramural care. Second, nursing staff questioned the proportionality of IPC measures, potentially influenced by behaviour of colleagues and patients. Third, inadequate information transfers about patients carrying MDROs. Fourth, uncertainty about the roles and responsibilities of involved healthcare professionals. Finally, the need for organisational support in providing education and sufficient resources, and clarity about financial aspects. Conclusions Insights from these studies have aided in aligning upcoming MDRO guidelines for home-based nursing care in the Netherlands, which factor in the above mentioned complexity. Furthermore, these findings can aid in future strategies to prevent or respond early in MDRO outbreaks with involvement of home-based nursing care.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".