Pharmacists practising in family medicine groups: An evaluation 2 years after experiencing a virtual community of practice
Bibliographic record
Abstract
Background: In 2018, a virtual community of practice (CoP) for pharmacists working in family medicine groups (FMGs) in Quebec province was developed. The aim of this CoP—called Réseau Québécois des Pharmaciens GMF (RQP GMF)—was to foster best practices by supporting FMG pharmacists. This study assesses the processes and outcomes of this CoP 2 years after its creation. Methods: We performed a cross-sectional web-based study from March to May 2020. All FMG pharmacists who were registered as members of the RQP GMF ( n = 326) were sent an invitation via a newsletter. The link to the questionnaire was also publicized in the CoP Facebook group. The questionnaire comprised a 38-item validated instrument assessing 8 dimensions of the CoP. A descriptive analysis was performed. Results: A total of 112 FMG pharmacists (34.4%) completed the questionnaire. Respondents agreed that the RQP GMF was a joint enterprise (mean score, 4.18/5), that members shared their knowledge (mean score, 3.94/5) and engaged mutually (mean score, 3.50/5) and that the RQP GMF provided support (mean score, 3.92/5) and capacity building (mean score, 4.01/5). In general, they were satisfied with the implementation process (mean score, 3.68/5) and with activities proposed (mean score, 3.79/5). A lower proportion of respondents agreed that their participation in the RQP GMF generated external impacts, which led to a smaller mean score (3.37/5) for this dimension. Conclusion: The RQP GMF, one of the first communities of practice for pharmacists practising in family medicine groups, attained most of the objectives initially intended by the CoP. These results will facilitate the adaptation of processes and activities to better fulfil members’ needs. Can Pharm J (Ott) 2021;154:xx-xx.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".