Creating, Delivering and Evaluating an Informatics Course
Bibliographic record
Abstract
Background: Due to changes in the scope and nature of care provided by pharmacists, all accredited Canadian Pharmacy programs were required to transition from a 4-year BSc to a 5-year PharmD by 2020. At the University of Manitoba the PharmD program launched in Fall 2019 and included the creation of a new 1-CR Pharmacy Informatics course. In late 2018, the Pharmacy liaison librarian was asked to be part of the planning team and soon was tasked to take the lead role. This presentation will present the challenges and opportunities associated with taking on an expanded role of this nature. Description: During the nine month planning process, the liaison librarian took a leading role in outlining the entire structure of the course, writing the syllabus, mapping out content and objectives to competencies and standards, creating all assessments, delivering the majority of the content, and marking and providing feedback. This session will also delve into the practical aspects of being an instructor that are not often discussed by librarians, such as figuring out honorifics, convincing students that not everything can be evaluated via a multiple choice question, how presentation styles and tone change when moving from a librarian to an instructor role, dealing with academic integrity issues in a professional program, and how to avoid teaching without being distracted by the thought of student evaluations. Conclusion: Assessment of the initial course was done through two means: student evaluations and faculty feedback. Student evaluations were used to better understand their perspective on the quality of the course and modify future offerings. Faculty feedback has been incredibly positive; the librarian has developed a much stronger relationship with the college with hours of instruction to the college increasing from 2 hours to 10, has been invited to participate in future course design, and taught the course again in 2021. Ultimately, this presentation will show another way in which librarians can demonstrate their value to administrators, which can be accomplished by broadening the instructional roles that liaisons play.
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 | 0.030 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".