Award Recognition for ADAPT Continuing Education Program for Pharmacists
Bibliographic record
Abstract
The Canadian Association of University Continuing Education (CAUCE) has recognized the ADAPT continuing education (CE) program for pharmacists with a 2012 Award for Program Excellence. Every year, CAUCE presents awards for excellence in program development in university continuing education. This year, the ADAPT program received top marks in the category of Non-Credit University Programming over 48 hours. ADAPT was developed by the Canadian Pharmacists Association (CPhA), the Canadian Society of Hospital Pharmacists and the University of Waterloo. It is a CE program that helps pharmacists develop knowledge and skills in medication management and collaborative patient care. The program is aimed at building expertise among pharmacists at a time when scopes of practice are expanding and when more pharmacists are part of interprofessional primary health care teams, says Phil Emberley, Director of Pharmacy Innovation at CPhA. “By combining best practices in online education, evidence-based content and transformative learning, ADAPT gives pharmacists the specialized knowledge, skills and confidence they need to care for patients in this evolving health care environment,” he says. “ADAPT is unique in the Canadian pharmacy landscape, and we are very pleased to see it recognized with this award.” Funded in part by Health Canada, ADAPT was extensively piloted in late 2010 and graduated its first full class in December 2011. More information on the program is available at www.pharmacists.ca/adapt.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.135 | 0.046 |
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".