Consensus-based antimicrobial resistance and stewardship competencies for UK undergraduate medical students
Bibliographic record
Abstract
Background: In the UK there is limited coverage of antimicrobial stewardship across postgraduate curricula and evidence that final year medical students have insufficient and inconsistent antimicrobial stewardship teaching. A national undergraduate curriculum for antimicrobial resistance and stewardship is required to standardise an adequate level of understanding for all future doctors. Objectives: To provide a UK national consensus on competencies for antimicrobial resistance and stewardship for undergraduate medical education. Methods: Using the modified Delphi method over two online survey rounds, an expert panel comprising leads for infection teaching from 25 UK medical schools reviewed competency descriptors for antimicrobial resistance and stewardship education. Results: There was a response rate of 100% with all 28 experts who agreed to take part completing both survey rounds. Following the first-round survey, of the initial 55 descriptors, 43 reached consensus (78%). The second-round survey included the 12 descriptors from the first-round in which agreement had not been reached, four amended descriptors and 12 new descriptors following qualitative feedback from the panel members. Following the second-round survey, a total of 58 consensus-based competency descriptors within 6 overarching domains were identified. Conclusion: The consensus-based competency descriptors defined here can be used to inform standards, design curricula, develop assessment tools and direct UK undergraduate medical education.
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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 | 0.059 | 0.136 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".