Strategies used throughout the scaling-up process of eConsult – Multiple case study of four Canadian Provinces
Bibliographic record
Abstract
BACKGROUND: eConsult is a model of asynchronous communication connecting primary care providers to specialists to discuss patient care. This study aims to analyze the scaling-up process and identify strategies used to support scaling-up efforts in four provinces in Canada. METHODS: We conducted a multiple case study with four cases (ON, QC, MB, NL). Data collection methods included document review (n = 93), meeting observations (n = 65) and semi-structured interviews (n = 40). Each case was analyzed based on Milat's framework. RESULTS: The first scaling-up phase was marked by the rigorous evaluation of eConsult pilot projects and the publication of over 90 scientific papers. In the second phase, provinces implemented provincial multi-stakeholder committees, institutionalized the evaluation, and produced documents detailing the scaling-up plan. During the third phase, efforts were made to lead proofs of concept, obtain the endorsement of national and provincial organizations, and mobilize alternate sources of funding. The last phase was mainly observed in Ontario, where the creation of a provincial governance structure and strategies were put in place to monitor the service and manage changes. CONCLUSIONS: Various strategies need to be used throughout the scaling-up process. The process remains challenging and lengthy because health systems lack clear processes to support innovation scaling-up.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 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".