Lessons from the field: Supporting infection prevention and control and antimicrobial stewardship in Amman, Jordan
Bibliographic record
Abstract
BACKGROUND: To reduce antimicrobial resistance (AMR), appropriate antimicrobial prescribing is critical. In conjunction with Infection Prevention & Control (IPC) programs, Antimicrobial Stewardship Programs (ASP) have been shown to improve prescribing practices and patient outcomes. Low- and middle-income countries (LMIC) face challenges related to inadequate ASP policies and guidelines at both the national and healthcare facility (HCF) levels. METHODS: To address this challenge, the World Health Organization (WHO) created a policy guidance and practical toolkit for implementation of ASPs in LMIC. We utilized this document to support a situational analysis and two-day ASP-focused workshop. In follow-up, we invited these attendees, additional HCF and hospital directors to attend a workshop focused on the benefits of supporting these programs. RESULTS: Over the course of a total three days, we recruited hospital directors, ASP team members, and IPC officers from fifteen different healthcare facilities in Jordan. We describe the courses and coordination, feedback from participants, and lessons learned for future implementation. CONCLUSIONS: Future efforts will include more time for panel-type discussion. which will assist in further delineating enablers and barriers. Also planned is a total three-day workshop; with the first two days being with ASP/IPC teams, and the final third day being with hospital directors and leadership. The WHO policy guidance and toolkit are useful tools to address overuse of antimicrobial agents. Strong leadership support is needed for successful implementation of ASP and IPC. Discussions on quality/safety, as well as cost analyses, are important to generate interest of stakeholders.
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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 | 0.015 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".