Integrating the "New" with the "Traditional": An Innovative Education Model
Bibliographic record
Abstract
In 1998 the Faculty of Pharmacy and Pharmaceutical Sciences at the University of Alberta, Canada, introduced a new elective semester course entitled "The Pain Module" for pharmacy students in their final year of undergraduate training. The aim was to build on the existing theoretical content related to areas such as pharmacology and therapeutics and to generate opportunities for skill and attitude development, including those related to the management of cancer-related pain. Traditional formats such as classroom-based seminars were integrated with innovative methods such as computer-mediated discussions and conferencing (CMC) and academic bus rounds. The CMC component of the course served to provide continuity of discussion between weekly classroom discussions, gave students access to content experts on an ongoing basis and furthered learning initiated in the classroom. Students were given the opportunity to meet palliative care patients being cared for at home and in hospices. A total of 21 students participated in all the course activities. By the end of the course, there appeared to be a greater appreciation for end-of-life care issues. This highlights the need to incorporate end-of-life care into the undergraduate curriculum of disciplines other than medicine and nursing. Evaluation of the course identified several benefits and limitations of CMC. There was increased access to content experts and increased interaction between students. Limitations of CMC included increased time commitments and an open-ended nature that was uncomfortable for some learners. Other benefits and limitations are described further in this article. Future attempts at integrating new instructional technologies should be systematically evaluated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".