Travel Medicine Curricula across Canadian Pharmacy Programs and Alignment with Scope of Practice
Bibliographic record
Abstract
Limited research exists on pharmacy students' training in travel medicine, and how this aligns with scope of practice. This research aimed to detail travel medicine education across pharmacy programs in Canada and map this against the scope of practice for pharmacists in each university's jurisdiction. A survey based on the International Society of Travel Medicine's Body of Knowledge was developed and distributed to all Canadian undergraduate pharmacy schools to identify topic areas taught, teaching modalities utilized, and knowledge assessment performed. Educational data was collected and analyzed descriptively, and compared to pharmacists' scope of practice in the province in which each university is located. Training provided to students varied significantly across universities and topic areas, with topics amenable to self-care (e.g., traveller's diarrhea and insect bite prevention) or also encountered outside of the travel context (e.g., sexually transmitted infections) taught more regularly than travel-specific topics (e.g., dengue and altitude illness). No apparent relationship was observed between a program's curriculum and their provincial scope of practice. For example, training in vaccine-preventable diseases did not necessarily align with scope related to vaccine administration. Alignment of education to current and future scope will best equip new practitioners to provide care to travelling patients.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".