Spontaneous Scaling of a Primary Care Innovation in Real-Life Conditions: Protocol for a Case Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Scaling effective primary care innovations to benefit more people is of interest to decision makers. However, we know little about how promising innovations are being scaled “spontaneously,” that is, without deliberate guidance. OBJECTIVE We aim to observe, document, and analyze how, in real-life conditions, 1 primary care innovation spontaneously scales up across Quebec, Canada. METHODS We will conduct a participative study using a descriptive single-case study. It will be guided by the McLean and Gargani principles for scaling and reported according to the COREQ (Consolidated Criteria for Reporting Qualitative Research) guidelines. Informed by an integrated knowledge translation approach, our steering committee will include patient users throughout the project. Inspired by the Quebec College of Family Physician’s “Dragons’ Den” primary care program, we will identify a promising primary care innovation that is being or will be scaled spontaneously. We will interview the innovation team about their scaling experiences every month for 1 year. We will conduct interviews and focus groups with decision makers, health care providers, and end users in the innovation team and the target site about their experience of both scaling and receiving the scaled innovation and document meetings as nonparticipant observers. Interview transcripts and documentary data will be analyzed to (1) compare the spontaneous scaling plan and implementation with the McLean and Gargani principles for scaling and (2) determine how it was consistent with or diverged from the 4 McLean and Gargani guiding principles: justification, optimal scale, coordination, and dynamic evaluation. RESULTS This study was funded in March 2020 by the Canadian Institutes of Health Research. Recruitment began in November 2023 and data collection began in December 2023. Results are expected to be published in the first quarter of 2024. CONCLUSIONS Our study will advance the science of scaling by providing practical evidence–based material about scaling health and social care innovations in real-world settings using the 4 guiding principles of McLean and Gargani. INTERNATIONAL REGISTERED REPORT PRR1-10.2196/54855
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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 | 0.084 | 0.087 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.086 | 0.020 |
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".