Towards sustainable care for MDRO carriers: network analyses to reveal information exchange pathways
Bibliographic record
Abstract
Abstract Background To prevent the spread of multidrug-resistant organisms (MDROs), medical information exchange between healthcare professionals about MDRO carriage is essential to timely initiate infection prevention and control measures. This study investigates information exchange about MDRO carriage between healthcare professionals and to explores the perceived clarity about collaborative roles and responsibilities. Methods Quantitative methods were used to explore information exchange networks through an online survey. A realistic clinical scenario was presented to healthcare professionals in primary, secondary and home-based nursing care (N = 122) reflecting their current daily practice. Social network analysis techniques were used to analyse the networks on a network and node level, which provided insight into collaborative structures and potential brokers. Additionally, respondents reflected on the (clarity of) roles and responsibilities of all involved, and on current frameworks and guidelines. Results Information exchange structures were visualized in a network graph, illustrating the information flow following established MDRO carriage during, or after recent, hospital admission. Several healthcare professionals were identified who could potentially take on a brokerage role in information exchange towards home-based nursing care. Healthcare professionals expressed a need for more clarity about roles and responsibilities, more uniform and timely information exchange, and indicated a preference as to which healthcare professionals could take on a coordinating role. Conclusions Current information exchange structures have a promising basis to achieve effective communication about MDRO carriage between healthcare professionals in different care settings. Improving uniformity and clarity about roles and responsibilities, could aid in timely initiation of infection prevention and control measures and contribute to sustainable care for MDRO carriers. Key messages • Information exchange between healthcare professionals about MDRO carriage is essential to ultimately prevent transmission and spread of MDROs. • Improving uniformity of information exchange and clarity about roles and responsibilities is needed to move towards sustainable care for MDRO carriers.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".