Bibliographic record
Abstract
scope and expertise, in regulatory standards.Embracing change is essential to managing it.Infrastructure and resources are necessary enablers to implementing change, and measurement and reporting are important tools for communicating the impact of change.CSHP 2015 has provided a framework for practice excellence and measurement of progress.The Canadian National Clinical Pharmacy Key Performance Indicators Collaborative, in partnership with CSHP, will soon be providing knowledge translation for measurement and reporting of outcomes.The Association of Faculties of Pharmacy of Canada, partially supported by the Blueprint for Pharmacy, is creating a preceptor development program and models of experiential education to facilitate the growing demand for student training.Through these times of change and opportunity, CSHP will continue to support its members by seeking answers to the issues that challenge us: What will pharmacy look like in the future?What will be the needs of pharmacists in hospitals and collaborative health care settings, in terms of education, guidelines, specialization, and professional recognition?How will pharmacy practice be structured and regulated?These and other questions will inform the new CSHP strategic plan and will be the focus of activities in the 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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.021 | 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".