Assessment of scalability of evidence-based innovations in community-based primary health care: a cross-sectional study
Bibliographic record
Abstract
Background: In 2013, the Canadian Institutes of Health Research funded 12 community-based primary health care research teams to develop evidence-based innovations. We aimed to explore the scalability of these innovations. Methods: In this cross-sectional study, we invited the 12 teams to rate their evidence-based innovations for scalability. Based on a systematic review, we developed a self-administered questionnaire with 16 scalability assessment criteria grouped into 5 dimensions (theory, impact, coverage, setting and cost). Teams completed a questionnaire for each of their innovations. We analyzed the data using simple frequency counts and hierarchical cluster analysis. We calculated the mean number and standard deviation (SD) of innovations that met criteria within each dimension that included more than 1 criterion. The analysis unit was the innovation. Results: The 11 responding teams evaluated 33 evidence-based innovations (median 3, range 1–8 per team). The innovations focused on access to care and chronic disease prevention and management, and varied from health interventions to methodological innovations. Most of the innovations were health interventions (n = 21), followed by analytical methods (n = 4), conceptual frameworks (n = 4), measures (n = 3) and strategies to build research capacity (n = 1). Most (29) met criteria in the theory dimension, followed by impact (mean 22.3 [SD 5.6] innovations per dimension), setting (mean 21.7 [SD 8.5]), cost (mean 17.5 [SD 2.1]) and coverage (mean 14.0 [SD 4.1]). On average, the innovations met 10 of the 16 criteria. Adoption was the least assessed criterion (n = 9). Most (20) of the innovations were highly ranked for scalability. Interpretation: Scalability varied among innovations, which suggests that readiness for scale up was suboptimal for some innovations. Coverage remained largely unaddressed; further investigation of this critical dimension is necessary.
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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 | 0.141 | 0.224 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".