Lynne M. Sylvia and Judith T. Barr. Pharmacy Education: What Matters in Learning and Teaching
Bibliographic record
Abstract
Lynne M. Sylvia and Judith T. Barr. Pharmacy Education: What Matters in Learning and Teaching. Sudbury, MA: Jones & Bartlett Learning; 2011. 341 pp, $51.95 (paperback), ISBN 97807673977. Most pharmacy educators are either clinicians or scientists who are passionate about their careers and desire help mold new pharmacists just as their teachers and mentors shaped them. Although the leap from practice academia may be as simple as accepting a different job, new teachers often feel overwhelmed and underprepared as they face the stares of students who appear no more engaged at the end of a lecture than when they first walked into the classroom. This leaves new educators wondering what they have gotten themselves into and whether they will actually be able make a difference in the lives of students. They quickly come the realization that may not be as easy as it first looked. Unlike grade school and high school teachers, few instructors in higher education received formal training in be a Sylvia and Barr have come the rescue with their new book, Pharmacy Education: What Matters in Learning and Teaching. This primer has been specifically written for pharmacy educators using discipline-specific examples. It is a welcome addition the small number of offerings of pharmacy-specific educational literature, but would also be a helpful reference for higher education teachers in other areas, especially in health professional programs. The book discusses all aspects of teaching, beginning with what makes a good teacher and expanding educational theory and analysis of learning styles, and makes the case for a student-centered approach teaching. Sylvia had me hooked on the second page where she states teaching and learning do not exist as separate and distinct activities.. .the facilitation and promotion of learning, rather than the transmission or telling of knowledge, should be at the forefront of all your actions as a teacher. This is what I have come believe in my 8 years in academia, but facilitation is easier said than done. This book provides ample opportunities for the reader learn the building blocks of facilitating and promoting learning. It includes chapters on assessment, large classroom and small-group teaching, application of technology, and specific guidance for laboratory and experiential teaching. The book also includes a chapter on a topic that is not usually covered in how to manuals and which is especially helpful for faculty members in professional educational programs: What Matters in Developing Professionals and Professionalism. This chapter discusses the need incorporate education about professionalism into the curriculum and suggests strategies promote civility and professionalism along with advice on handle unprofessional behavior. Numerous examples that are integral the learning points of the chapter and which the reader can readily relate are provided throughout the text. In particular, chapter 9, What Matters in Experiential Education, contains 2 examples that are easily transferable my own experience as an acute care internal medicine preceptor. Although I have been a pharmacy preceptor for many years, Sylvia offered new ways of enhancing student learning that I can implement tomorrow in my clinical practice as I work with students. …
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.055 | 0.045 |
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".