Bibliographic record
Abstract
J C P H – Vol. 57, n 2 – avril 2004 136 and drug information. CSHP thus has a wealth of credibility to draw upon. Speaking with one voice is also important. CSHP enjoys a strong volunteer membership base, and over the past few months, many members in each of the Society’s Branches have written letters to legislators, bureaucrats, and regulators. These letters have conveyed a single message — that hospital pharmacists make a difference. Speaking as a collective with a consistent message, one that puts the patient’s well-being at the centre of our practice, is a strong and powerful tool for advancing our profession. Finally, we must speak when the opportunity arises. Knowing when to speak up — seizing the moment — is critical. In times of crisis, people turn to organizations they can trust, organizations like CSHP. By speaking out when the time is right and by demonstrating our capable and proven leadership through the consistent voice of our members, we can make great gains. Are we being opportunistic in the patient safety dialogue? Yes, we are! CSHP’s Vision sets the goal of being the influential voice for hospital pharmacy, so we must speak together, with credibility and conviction, to put hospital pharmacy on the health care stage.
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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.048 | 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".