Canadian Pharmacists Conference 2017 pharmacy practice research highlights
Bibliographic record
Abstract
Canadian Pharmacists Conference 2017, the largest annual gathering of Canadian pharmacists, is a unique opportunity for more than 700 researchers, practitioners and industry representatives to interact face to face with thought leaders in the field and to share practical information and tools relevant to front-line pharmacists, as well as the latest in pharmacy practice research.Results from local trials and multisite research projects, including new approaches to medication management, innovative care models and patient services under expanded scope of practice, were presented in 20 oral and more than 50 poster research presentations featured at this year's conference in Quebec City, June 2-5, 2017.Two research studies stood out as the winners in this year's research competition: the UBC research on perceptions of pharmacy students involved in preventative health and wellness events (Best Poster) and the University of Waterloo research on pharmacist perceptions of a changing scope of practice before it happens (Best Oral Research Presentation). 1,2 Below are some other highlights of the research findings presented at this year's conference.In some cases, studies presented were considered preliminary.Many will be analyzed more thoroughly, and likely published in peerreviewed journals.
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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.152 | 0.022 |
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".