Evidence‐based medicine among the dreaming spires of Oxford: the Pfizer Pharmacy Grant 2014
Bibliographic record
Abstract
In November 2014, I attended a short course at Oxford University, Teaching Evidence-Based Practice, with assistance from an SHPA grant. The course was run by the Centre for Evidence-Based Medicine (CEBM) and the University of Oxford Department for Continuing Education. The CEBM is a recognised world leader in evidence-based practice. This was an intensive course for those who already have skills in evidence-based medicine, and focused on the teaching of critical appraisal and evidence-based practice. The NSW Medicines Information Centre (MIC) runs courses for pharmacists in Medicines Information. Critical appraisal is an integral part of both the introductory and advanced courses offered by the MIC. I wanted to attend the course to improve my teaching skills in an area that many people find dry and overly technical. The course is part of Oxford's Master in Science (MSc) in Evidence-Based Health Care or Postgraduate Diploma in Health Research, but is also available as a stand-alone professional development course. The CEBM courses attract participants from around the world with attendees from Europe, America, Canada, Scandinavia, Southeast Asia and Australia. They were from primary and secondary care, academia, medical and non-medical backgrounds, and junior and senior positions; one was a current Rhodes Scholar. Most were involved in teaching of some kind ranging from formal university and clinical teaching to responsibility for peer programs and courses. This wide variety of backgrounds and experience allowed for stimulating cross-pollination of ideas. The course was designed to equip attendees to teach evidence-based practice and to develop effective and relevant educational curricula in evidence-based health care. Individual guidance was given to extend critical appraisal and teaching skills. The course was divided into formal plenary sessions, and small group work. Plenaries covered searching methods, teaching critical appraisal, learning styles, statistics, curriculum development and evaluation. Most plenary sessions combined a teaching demonstration with commentary on the methods used and alternatives. Emphasis was placed on engaging participants and demystifying the process of appraisal. Particular attention was given to the teaching of statistics, which many people find daunting. Each attendee gave at least two presentations to his or her tutorial group, one of which was on statistics. Preparation time for the statistics presentation was deliberately limited. Criticism of presentations was focused on improvement and identifying strengths, making the process educational rather than threatening. The topics were varied, reflecting the difference in backgrounds of the participants. This diversity was a real bonus. One doctor commented at the end of the course that she had initially been disappointed at being the only clinician in the group, but that she had learnt enormously from seeing other professional viewpoints. The course was both stimulating and practical. It has given me more confidence to teach evidence-based practice. I plan to use the skills learnt to develop a short course for pharmacists and a set of online tutorials. I would like to acknowledge the financial support of the Society of Hospital Pharmacists of Australia and Pfizer Australia Pty Ltd. Without this support, I would not have been able to attend the course. Leone M. Snowden, B. Pharm NSW Medicines Information Centre, St Vincent's Hospital Sydney, Sydney, Australia E-mail: [email protected]
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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 | 0.040 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".