Translating Technology in Professional Practices to Optimize Infection Prevention and Control
Bibliographic record
Abstract
The aim of this study was to explain how the Polymerase Chain Reaction (PCR) technology was translated into professional practices to prevent and control vancomycin-resistant enterococci outbreaks via an actor-network, based on the integrated framework TRIP-ANT. A single case study was conducted in three purposefully selected sites implementing the PCR-VRE assay. The complete dataset comprised semi-structured interviews with 28 participants and a review of hospital and external documents. A content analysis was conducted. The authors' findings indicate the emergence of four main themes, including illustration of who was involved in the adoption process, attribution of roles and responsibilities, interaction/communication/ collaboration mechanisms, and changes in professional practices. Their findings also address five challenges that arose from each theme. The translation of PCR technology into professional practices relies on the enrolment of an organisational, clinical, managerial and financial support network, and on the evolution of practices, communications, and roles and responsibilities.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".