Physical assessment educational programs for pharmacists and pharmacy students: A systematic review
Bibliographic record
Abstract
Abstract Introduction The scope of pharmacy practice has evolved to include physical assessment (PA) as part of the management of drug therapy. Objective To describe programs developed to teach pharmacists/pharmacy students PA and identify factors associated with improved knowledge, skill, confidence, and utilization. Methods A librarian‐assisted search was performed of MEDLINE, Embase, and Cumulative Index to Nursing and Allied Health Literature using the terms “pharmacist,” “student,” and “physical examination,” supplemented with manual reference searches. Studies published exclusively as abstracts were excluded. No language restrictions were applied. Extracted data included design, location, participants, methods of instruction, PA skills taught, assessment, utilization, and follow‐up. Results The search yielded 635 unique citations, which were independently reviewed by two authors. Twenty‐eight articles were reviewed in full and 16 were included. Most studies were conducted in the United States or Canada. Thirteen studies enrolled pharmacy students (mainly second or third year), which focused on comprehensive PA skills or blood pressure measurement. Length of instruction ranged from a single session to a full‐year course. Generally, any type of instruction improved knowledge and confidence with PA. Students preferred pharmacist instructors to other clinicians, and live subjects to simulators/manikins. Three studies evaluated courses for practicing pharmacists, which included comprehensive PA instruction and consisted of 2‐30 contact hours. Participants' confidence with performing PA improved from precourse to postcourse surveys. One study showed improved confidence with performing PA 6 months after the course, while another study showed no improvement in confidence but increased PA use at 6 months postcourse. Utilization of PA after 6 months ranged from 49% to 66%. Conclusions A variety of programs have been developed to teach PA skills to pharmacists/pharmacy students. Limited data suggest that sessions which included pharmacist instructors and live subjects to practice PA skills were preferred. Courses for practicing pharmacists improved confidence with performing PA, but persistent confidence and utilization at 6 months were variable.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".