Strategies used throughout the scaling-up process of eConsult – Multiple case study of four Canadian Provinces
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it