Bibliographic record
Abstract
JCPH – Vol. 63, n 6 – novembre–decembre 2010 460 (published in 2010 by the Institute for Safe Medi cation Practices Can ada and the Canadian Patient Safety Institute; see www.ismp-canada.org/ barcoding/download/ CanPharmBarcode_ JointTechnicalStatement.pdf). • The CSHP 2015 initiative has an entire goal focused on the application of technology to improve the safety of medication use (Goal 5) (see www.cshp.ca/dms/dmsView/2_CSHP-2015Goals-and-Objectives-Feb-25%2707-w-Appdx-revMay%2708.pdf). • CSHP successfully advocated for improvements to the Licensed Natural Health Products Database so that clinical decision-support tools could continue to include natural health products. • CSHP conducted an advocacy campaign on the implementation of unit-dose medication distribution systems to enhance patient safety. • CSHP has representatives on the National e-Pharmacy Task Force and the Blueprint for Pharmacy Steering Committee. • CSHP participated in the development of and endorsed the implementation plan of the Blueprint for Pharmacy (see www.pharmacists.ca/content/about_cpha/whats_happening/ cpha_in_action/pdf/BlueprintImplementationPlan.pdf ), which includes deliverables related to information and communication technology. The time for action is now! As a profession, we need to ensure that technology as a critical safety tool moves to the forefront. Otherwise, we may be waiting another 50 years before it becomes a reality.
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 | 0.029 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.031 | 0.049 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.016 | 0.017 |
| Insufficient payload (model declined to judge) | 0.050 | 0.019 |
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".