Bibliographic record
Abstract
CSHP is also responding to the changing needs of members for communication, participation, and learning by building and expanding digital communication channels.You can join CSHP on Facebook, participate in CSHP 2015 blogs and Twitter streams, and take advantage of the topical education webinars.During the PPC, check out award-winning residency projects, virtual posters, and student video competition entries, and consider participating in next year's awards programs.You can also connect with and learn from colleagues by joining one of CSHP's 23 Pharmacy Specialty Networks (PSNs), which cover diverse areas such as antimicrobial stewardship, drug utilization, emergency, home care, pediatrics, and transplantation.There is even a PSN specifically for pharmacists practising in small hospitals.Participating members rate PSNs as informative with regard to new practices and guidelines, ideas on how to better serve patients and share knowledge, connecting with peers across Canada, and more.What better way to connect efficiently and productively with colleagues with similar interests and expertise that they are willing to share?Check www.cshp.cafor information.Pharmacists are creative, innovative, and dedicated professionals, keen to collaborate to advance patient care.I encourage you to take the initiative: participate, lead, be engaged, and promote our profession.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".