Bibliographic record
Abstract
C J H P – Vol. 59, No. 4 – September 2006 J C P H – Vol. 59, n 4 – septembre 2006 challenging ourselves or our colleagues. Without the encouragement and support of my colleagues at CSHP, I know that I wouldn’t be here, facing this new challenge. CSHP’s Hospital Pharmacy Management Leadership Task Force, chaired by Robin Ensom, is exploring opportunities for CSHP to provide and support leadership development programs for hospital pharmacists. As the executive officer responsible for the Vision Portfolio, I will oversee Vision 2010. Being involved with Vision 2006 from the beginning has been both a learning and rewarding experience. Vision 2010 will introduce many exciting initiatives for the Society as a whole, and for our individual members. I can’t wait to share them with you! CSHP wants to engage its members in practice goals and objectives (which will include national benchmarking and targets) and will continue to advocate for hospital pharmacists at the national and branch levels. And of course, we’re still looking for ways to inspire, support, recognize, and reward our members. I believe it is very important for CSHP to recognize the “value” and “leadership potential” of our members. CSHP’s volunteers drive the organization, and they should feel appreciated! So come out and get involved. You could be a leader-in-waiting!
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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.056 | 0.027 |
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".