Evaluation of a <i>Dragons’ Den</i>–inspired symposium to spread primary health care innovations in Quebec, Canada: a mixed-methods study using quality-improvement e-surveys
Bibliographic record
Abstract
BACKGROUND: , at which innovators pitched their innovations to Dragon-Facilitators (i.e., decision-makers) and academic family medicine clinical leads. We evaluated the effects of the symposium on the spread of primary health care innovations. METHODS: We conducted a mixed-methods evaluation of the symposium. We collected data related to Rogers' innovation-decision process using 3 quality-improvement e-surveys (distributed between May 2017 and February 2018). The first survey evaluated spread outputs (innovation discovery, intention to spread, improvements) and was sent to all participants immediately after the symposium. The second evaluated short-term spread outcomes (follow-ups, successes, barriers) and was sent to innovators 3 months after the symposium. The third evaluated medium-term spread outcomes (spread, perceived impact) and was sent to innovators and clinical leads 9 months after the symposium. We analyzed the data using descriptive statistics, content analysis and joint display. RESULTS: Fifty-one innovators, 66 clinical leads (representing 42 clinics) and 37 Dragon-Facilitators attended the symposium. The response rates for the surveys were 61% (82/134) for the immediate post-symposium survey of all participants; 68% (21/31) for the 3-month survey of innovators; and 49% (48/97) for the 9-month survey of clinical leads and innovators. Immediately after the symposium, clinical leads and Dragon-Facilitators reported a high likelihood of adopting an innovation (mean ± standard deviation 8.02 ± 1.63 on a 10-point Likert scale) and 87% (53/61) agreed that they had discovered innovations at the symposium. Nearly all innovators (95%, 20/21) intended to follow up with potential adopters. After 3 months, 62% (13/21) of innovators had followed up in some way. After 9 months, 72% of clinical leads (18/25) had implemented at least 1 innovation, and 52% of innovators (12/23) had spread or were in the process of spreading innovations. INTERPRETATION: The innovation symposium supported participants in achieving the early stages of spreading primary health care innovations. Replicating such symposia may help spread other health care innovations.
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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 | 0.041 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".