Setting priorities for physical examination in pharmacy education: A Delphi study
Bibliographic record
Abstract
Background: As the scope of pharmacy practice is expanding, a growing number of pharmacists perform physical examination (PE) to gather additional information to monitor the effectiveness and safety of their patients’ therapy. This professional activity calls for the development of comprehensive and valuable PE training. We sought to determine by consensus which PE tests should be given teaching priority in pharmacy education. Methods: Using existing PE literature in pharmacy, we conducted an online Delphi survey from December 2021 to April 2022 with 16 pharmacists who practise in a variety of settings and/or who are considered experts in PE. Results: After 2 Delphi rounds, consensus was reached to either include or exclude 27 PE tests in entry-to-practice programs. One last round allowed prioritizing the agreed-upon PE tests in terms of educational needs. Clinicians agreed that measuring blood pressure is indispensable and should be given teaching priority, followed by pulse rate, weight and blood glucose measurements. Endocrine system and head and neck examinations should be included in pharmacy programs, but their clinical usefulness was considered less important. Discussion: We compared our results with PE literature in other health care disciplines. We found that only a few PE tests truly influence drug therapy management, that some examinations can be quite difficult to perform accurately and that without proper training and opportunities to retrain, skill decay can lead to dangerous misinterpretations. Pharmacy programs should consider focusing on teaching PE tests supported by evidence as having an impact on drug therapy management. Can Pharm J (Ott) 2024;157:xx-xx.
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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 | 0.091 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".