A Primary Care PBRN Impact and Strategic Clarity Initiative : Journey, Outcomes and Lessons Learned
Bibliographic record
Abstract
Context: The Réseau de recherche en soins primaires de l’ Université de Montréal (RRSPUM) is the Primary Care (PC) Practice-Based Research Network (PBRN) affiliated with the Department of Family Medicine and Emergency Medicine (DFMEM) of our University (Quebec, Canada). RRSPUM includes 19 Academic Family Medicine clinics. In 2020, as our PBRN was expanding and because of the rapidly evolving Quebec Primary PC research landscape, we embarked on a strategic planning exercise. Objective: To better define the RRSPUM mission, vision and 5-year action plan. Study design and Analysis: A rigorous social innovation approach. Setting: Université de Montréal DFMEM. Population studied: The RRSPUM organization and its membership. Intervention: An iterative social innovation process evolved over a 18-month period under the guidance of two experienced Innoweave strategic impact coaches. Three working committees participated in this endeavor. A “core” committee composed of the DFMEM director and research director, the RRSPUM director and one research facilitator saw to the timely unfolding of the different phases of the initiative and preparing all written documentation. Two “peripheral” committees, consulted at specific phases of the initiative, were set up to ensure discussion of the strategic positioning of the RRSPUM both within and outside our faculty. The first included DFMEM clinicians, researchers, a patient partner, and the second included representatives of the Quebec PC research landscape. Results: Three RRSPUM cornerstone deliverables were collectively determined: an intended impact statement, a theory of change and a set of strategic priorities and action plan. A RRSPUM theory of change graphic representation was created as well as an informed action plan to facilitate understanding of the process. Conclusion: RRSPUM is the first of the 4 Quebec PC PBRNs to undertake a rigorous coach-led strategic clarity initiative. This process increased the our 19 clinics’ sense of membership to RRSPUM as well as clarified their role in developing and supporting PC research in our DFMEM. We believe that sharing the documentation resulting from our strategic clarity reflection, in a user-friendly format, will encourage other PBRNs to undertake a similar approach or to draw inspiration from it to review their own action plans and clarify, in these times of uncertain infrastructure funding, the relationships to develop both within and outside their internal environments.
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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 | 0.056 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".