AFPC CPERC 2023 Abstracts – Oral and Poster Presentations
Bibliographic record
Abstract
Background: The healthcare system, including pharmaceuticals have a large impact on the environment and climate of our planet.There is growing interest by pharmacy students in understanding this impact, and identifying opportunities to reduce, mitigate, and adapt to the negative effects caused by the health care system.The amount of education being provided on the topic within the University of British Columbia's (UBC) Entry to Practice (E2P) PharmD current curriculum is limited.Goals: To develop and produce an educational module on the health care system's impact on the environment, targeted toward Professional Year 4 (PY4) E2P PharmD students at UBC.Description: A passionate Professional Year 3 E2P PharmD student performed an environmental scan and literature search on the topic and identified content experts to present on the following areas: 1) Introduction to Climate Change, Health and Health systems, 2) Pharmacists' role in planetary health, 3) Medication Waste in Hospitals and 4) Management of pharmaceutical waste in the community.Presentations were recorded and edited for clarity and time.The final presentations will be incorporated into an asynchronous PY4 E2P course starting in 2023.Supplementary resources (flow diagrams, links to reputable organizational resources and peer reviewed journal articles) were also created and collected to further students' self-education and engagement on the topic. Relevance to Pharmacy Education:The impact of the healthcare industry, and specifically pharmaceuticals on the environment is of increasing relevance and interest to pharmacy professionals.This educational module provides introductory information for pharmacy students and will become a component of a mandatory course within UBC's E2P program.Student and Faculty evaluation of this content will occur and may support an expansion into an elective course.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.926 | 0.778 |
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; the direct Gemma label and the distilled Codex classifier 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".