ANTIBIOTIC STEWARDSHIP PROGRAM; CONTEXTUAL ENVIRONMENT AND FRAMEWORK
Bibliographic record
Abstract
Introduction Antibiotic Stewardship is currently an important topic in Public Health, Hospital Epidemiology and Infectious Disease Epidemiology. Designing, implementing and evaluating an Antibiotic Stewardship Program (ASP) is mandates by accreditation agencies. Prairie North Regional Health Authority (PNRHA)- Saskatchewan assigned me to collect and analyse quantitatively and qualitatively - data related to antibiotic prescription habits of physicians in three hospitals and community clinics. Objectives 1- to review the literature on antibiotic Stewardship and its programs 2- to understand the local antibiotics prescribing environment (using one 2010 data) 3- to design an Antibiotic Stewardship Program for the PNRHA Methods Data collection by direct key stakeholders interviews, using hospital pharmacy, the lab and admission information systems. Community data were sourced from Saskatchewan Drug Plan administration - Using Excel and SPSS for data management, and biostatistics (parametric, non-parametric and variance analysis) - Using results for evidence based design of an Antibiotic Stewardship Program Results The detailed descriptive statistics were very helpful to draw a baseline. These descriptive statistics shed the light on usage of 33 antibiotics in three different hospitals. These results were categorized based on specialty, main groups of antibiotics, per doctor, per antibiotic, per DOT (Duration of Therapy) and DOT per 1000 patient-day. Conclusions There were recommendations on how to adopt the Antibiotic Stewardship and the suggested framework, as well as recommendations about data collection, analysis and measurements for future implementation of the Program. Specific steps were encouraged based on the exact results of specific antibiotics such as flouroquinolones, clindamycin, vancomycin and IV administration and conversion to oral antibiotics
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".