Bibliographic record
Abstract
J C P H – Vol. 68, no 4 – juillet–aout 2015 360 Board. I now look forward to joining the CSHP Executive Committee for my 3-year term as Vision Liaison, starting as President Elect for the coming year. Throughout my career, I have witnessed the growth of our profession and the impact of this change on patients’ health outcomes. Measures such as those published in the Hospital Pharmacy in Canada Report (www.lillyhospitalsurvey.ca/hpc2/content/rep_ 2015_toc.asp) and those included in the CSHP 2015 initiative (www.cshp.ca/cshp2015/index_e.asp) and the American Society of Health-System Pharmacists’ Pharmacy Practice Model Initiative (www.ashp.org/ppmi) help in monitoring progress toward objectives, comparing performance with benchmarks, assessing real value delivered to “customers”, and identifying improvement opportunities. Performance measures are crucial elements in driving change, as we can’t manage what we can’t measure! The key to successful strategy implementation will be to select the measures that best capture desired performance and then to review progress on these measures regularly and report developments to our members, supporters and stakeholders. The CSHP Strategic Plan 2015–2018 is our opportunity to contribute to the achievement of our vision and to put ideas for improvement into action. It is our opportunity to shape our Society for years to come!
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 | 0.028 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.026 | 0.018 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.017 | 0.026 |
| Insufficient payload (model declined to judge) | 0.042 | 0.016 |
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".