CHAPTER 2 introduction to evidence-based practice in evidence-based pharmacy 2nd edition
Bibliographic record
Abstract
This is an introduction to evidence-based practice for pharmacist. The following chapters will provide academic and practical information including tools, sources and resources for how to best practice evidence-based pharmacy. In this chapter we give some basic information and some examples of problems and limitations from studies and practices. What do we mean by “evidence-based clinical practice?” Evidence-based clinical practice describes the complex process of decision making and places it in the context of basing decisions on a systematic appraisal of the best evidence available. It requires a number of skills and an ability to: The challenge for evidence-based pharmacy is twofold. First to develop evidence-based medicine skills for use in clinical pharmacy whether in the secondary care setting on the ward or in primary care dealing with patients who present with problems or issues surrounding prescribed medicines. The second challenge is to find the evidence to support existing practice and to inform practice developments. Much has been written on this subject but two definitions are useful. The first from Professor David Sackett:1 > “ Evidence-based medicine is the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients ” > > Sackett et al 1 The authors go on to state that the practice of Evidence Based Medicine requires the integration of individual clinical expertise with the best available external clinical evidence from systematic research. The second definition is from a team at McMaster University in Canada: > “ …
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.075 | 0.032 |
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".