Co-design, implementation, and evaluation of an expanded train-the-trainer strategy to support the sustainability of evidence-based practice guides for registered nurses and social workers in primary care clinics: a developmental evaluation protocol
Bibliographic record
Abstract
BACKGROUND: The implementation of evidence-based innovations is incentivized as part of primary care reform in Canada. In the Province of Québec, it generated the creation of interprofessional care models involving registered nurses and social workers as members of primary care clinics. However, the scope of practice for these professionals remains variable and suboptimal. In 2019, expert committees co-designed and published two evidence-based practice guides, but no clear strategy has been identified to support their assimilation. This project's goal is to support the implementation and deployment of practice guides for both social workers and registered nurses using a train-the-trainer educational intervention. METHODS/DESIGN: This three-phase project is a developmental evaluation using a multiple case study design across 17 primary care clinics. It will involve trainers in healthcare centers, patients, registered nurses and social workers. The development and implementation of an expanded train-the-trainer strategy will be informed by a patient-oriented research approach, the Kirkpatrick learning model, and evidence-based practice guides. For each case and phase, the qualitative and quantitative data will be analyzed using a convergent design method and will be integrated through assimilation. DISCUSSION: This educational intervention model will allow us to better understand the complex context of primary care clinics, involving different settings and services offered. This study protocol, based on reflective practice, patient-centered research and focused on the needs of the community in collaboration with partners and patients, may serve as an evidence based educational intervention model for further study in primary care.
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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 | 0.179 | 0.159 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".