Bibliographic record
Abstract
J C P H – Vol. 57, n 3 – juin 2004 200 or by the College of Pharmacists of British Columbia. So should we just wait for other provinces to recognize the value of the pharmacists in managing drug therapy? Some might say “Sure — recognition will come, eventually,” but my answer is “No”. It is our job as professionals to promote best practice, to initiate programs to improve patients’ health outcomes, to gather evidence to support these programs, and to advocate for their implementation. CSHP can assist in these efforts, by supporting pharmacy practice studies through the Research and Education Foundation, promoting excellence through the awards program, setting practice and teaching standards, and lobbying decision makers. What we need for hospital pharmacists in all provinces are legislative frameworks like the ones in Quebec and British Columbia, which recognize our expertise and allow us to use all our skills. Let’s work together to achieve this goal, especially given the mounting Canadian evidence of adverse drug outcomes. Make yourself heard. Write to your pharmacy director, your hospital administrators, and your MP. As pharmacists, we need to enable change to decrease adverse drug outcomes.
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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 | 0.025 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.022 | 0.018 |
| Insufficient payload (model declined to judge) | 0.049 | 0.005 |
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".