Clinical Microbiology in Pharmacy Education: A Practice-based Approach
Bibliographic record
Abstract
The increasing incidence of multi-drug resistant pathogenic bacteria, alongside viral and fungal human pathogens, supports the argument that skills in microbiology and infectious disease diagnosis, treatment and prevention are of growing global importance to be held among primary care clinicians. In Canada, inevitable future astronomical health care costs largely due to an aging population, have forced eyes upon pharmacists as one of (if not) the primary clinical professions to accommodate the growing need to accommodate patient access to health care while maintaining lower health care costs. As such, the role of pharmacists in health care is expanding, punctuating the need to enhance and improve Pharmacy education. Accurate assessment of the current gaps in Pharmacy education in Canada provides a unique opportunity for a new Pharmacy School at the University of Waterloo to establish a non-traditional, outcomes-based model to curricular design. We are applying this iterative curriculum assessment and design process to the establishment of a Medical Microbiology program, deemed as a prominent gap in former Pharmacy educational training programs. A PILOT STUDY WAS CARRIED OUT DISTRIBUTING A COMPREHENSIVE SURVEY TO A LOCAL GROUP OF PHARMACISTS PRACTICING IN A VARIETY OF SETTINGS INCLUDING: hospital, clinic, community, independent, industry and government, to assess perceived gaps in Pharmacy microbiology and infectious disease education. Preliminary findings of the surveys indicate that practitioners feel under-qualified in some areas of microbiology. The results are discussed with respect to a curricular redesign model and next steps in the process of curricular design are proposed.
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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 | 0.044 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| 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".