Decisional Issues in Antibiotic Prescribing in French Nursing Homes: An Ethnographic Study
Bibliographic record
Abstract
Background Medication prescription is generally the responsibility of doctors. In nursing homes, the nursing staff is often the first to suspect an infection. Today, physicians are more confident with nursing assessment, relying primarily on nursing staff information. Very few studies have investigated the nurses’ influence on decision of medical prescription. This study investigates the role of nurses in antibiotic prescribing for the treatment of suspected infections in nursing home residents. Design and methods An ethnographic study based on semi-structured interviews and participant observations was conducted. Sixteen nurses and five doctors working in five nursing homes in Paris, France participated between October 2015 and January 2016. Results Given their proximity to elderly residents, registered nurses at the nursing homes occasionally assisted doctors in their medical diagnostic. However, nurses who are theoretically incompetent have met difficulties in their ability to participate in their decisions to prescribe antibiotics when managing residents’ infections. Conclusion if proximity and nursing skills reinforce the relevance of the clinical judgment of nurses, the effective and collaborative communication between the nurse and the doctor may help the nurse to enhance their role in the antibiotic prescribing in nursing homes, which would enhance antimicrobial stewardship efficiency.
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How this classification was reachedexpand
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 | 0.058 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.007 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".