Bibliographic record
Abstract
JCPH – Vol. 67, n 1 – janvier–fevrier 2014 92 that, without the injection of new members, CSHP’s membership would disappear entirely within 4 to 5 years. However CSHP has so far been successful in attracting new members to over-compensate for the loss. A closer look at these metrics shows that the first 2 years of membership are the most critical in determining if a member will find value in what CSHP offers. In this regard, the retention rate for members who renew beyond their second year of active membership is a healthy 69%. In contrast, the retention rate drops to 60% among first-year pharmacist members and to 35% among pharmacy residents and postgraduate students transitioning from training to “Active” membership. Energy and focus need to be directed to these target groups to help them find value in CSHP membership. As I learned at the Governance Summit, a successful 21st century association is one that can focus on the value that it provides to its members. CSHP is a powerful organization that supports its members in the development and pursuit of excellence in hospitals and other collaborative health care settings, as confirmed by our growing membership. However, the Society’s ability to continue to lead practice will be undermined if we do not capture the loyalty of hospital pharmacists as they transition from educational and training programs to practice. To paraphrase Albert Einstein, let’s try not to become an association of success, but rather to become an association of value.
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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".