The Creation of a Practice-Based Network of Pharmacists Working in Family Medicine Groups (FMG)
Bibliographic record
Abstract
A needs assessment study of pharmacists working in family medicine groups (FMG) demonstrated the necessity to build a practice-based network. This network would foster a faster integration into FMG and a more efficient collaborative practice. It would also take advantage of an existing practice-based research network (PBRN)—the STAT (Soutien Technologique pour l’Application et le Transfert des pratiques novatrices en pharmacie) network. A working group of nine FMG pharmacists from the different regions of the province of Quebec, Canada, and a committee of partners, including the key pharmacy organizations, were created. Between January 2018 and May 2019, nine meetings took place to discuss the needs assessment results and deploy an action plan. The practice-based network first year activities allowed identifying pharmacists working in FMGs across the province. A directory of these pharmacists was published on the STAT network. The vision, mission, mandate, name («Réseau Québécois des Pharmaciens GMF») and logo were developed. The first few activities include: Bi-monthly newsletters; a mentorship program; short evidence-based therapeutic letters (pharmacotherapeutic capsules) and a start-up kit to facilitate integration of these pharmacists. The Quebec FMG pharmacist practice-based network has been launched. It is planned to evaluate the members’ satisfaction in late Spring 2020 with regards to activities and resources provided.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".