Assessing the scalability of innovations in primary care: a cross-sectional study
Bibliographic record
Abstract
Background: Canadian health funding currently prioritizes scaling up for evidence-based primary care innovations, but not all teams prepare for scaling up. We explored scalability assessment among primary care innovators in the province of Quebec to evaluate their preparedness for scaling up. Methods: We performed a cross-sectional survey from Feb. 18 to Mar. 18, 2019. Eligible participants were 33 innovation teams selected for the 2019 Quebec College of Family Physicians’ Symposium on Innovations. We conducted a Web-based survey in 2 sections: innovation characteristics and the Innovation Scalability Self-administered Questionnaire. The latter includes 16 criteria (scalability components) grouped into 5 dimensions: theory (1 criterion), impact (6 criteria), coverage (4 criteria), setting (3 criteria) and cost (2 criteria). We classified innovation types using the International Classification of Health Interventions. We performed a descriptive analysis using frequency counts and percentages. Results: Out of 33 teams, 24 participated (72.7%), with 1 innovation each. The types of innovation were management (15/24), prevention (8/24) and therapeutic (1/24). Most management innovations focused on patient navigation (9/15). In order of frequency, teams had assessed theory (79.2%) and impact (79.2%) criteria, followed by cost (77.1%), setting (59.7%) and coverage (54.2%). Most innovations (16/24) had assessed 10 criteria or more, including 10 management innovations, 5 prevention innovations and 1 therapeutic innovation. Implementation fidelity was the least assessed criterion (6/24). Interpretation: The scalability assessments of a primary care innovation varied according to its type. Management innovations, which were the most prevalent and assessed the most scalability components, appear to be most prepared for primary care scale-up in Canada.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".