Bibliographic record
Abstract
understood things the way we do, he or she would want to help us succeed. In other words, we want to advocate through education. However, what we may be missing is an understanding of the minister’s or CEO’s needs. What the Advocacy Committee has learned is that many key decision-makers require a single question or “ask”. The “ask” should express exactly what we need, like the 2-yearold who needs a toy. The “ask” should require a specific action to be taken and should benefit both the decision-maker and the requester. When decision-makers see that working with us is to their benefit as well as ours, both parties are more likely to get what they want. National and branch volunteers and staff within CSHP are advocating on your behalf—we want to make CSHP the advocate for hospital pharmacists, and we need your trust, assistance, and ideas to ensure that CSHP continues to be the national voice of pharmacists committed to patient care through the advancement of safe, effective medication use in hospitals and other collaborative health care settings. My “ask” to all of you is for your continued support of CSHP advocacy for hospital pharmacists. You show this support through your membership and by volunteering with CSHP at the chapter, branch, and national levels. For this, I thank you, and I am proud to advocate with you.
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 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.056 | 0.052 |
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".