Champions as catalysts for change: a realist evaluation of their impact in primary care
Bibliographic record
Abstract
BACKGROUND: The healthcare system, particularly primary care, is constantly evolving to adapt to technological innovations and demographic changes. Quality improvement (QI) initiatives are a key strategy for optimizing care processes and better meeting patient needs. However, their success depends on several contextual factors, including alignment with organizational priorities, resource availability, and stakeholder engagement. While champions are widely recognized as important for change within organizations, the specific causal relationship of their impact remain unclear. This study aims to deepen understanding of champions’ behaviours in the QI process within interdisciplinary primary care settings. METHOD: A multiple case study was conducted using a realist evaluation approach to refine and validate the program theory. Data collection involved document analysis and individual semi-structured interviews. Context-Mechanism-Outcome (CMO) configurations were constructed to explore relationships between context (pre-existing elements shaping interventions), mechanisms (processes activated in a given context), and outcomes to iteratively refine the program theory. The study was conducted in primary healthcare clinics, the predominant model of primary care clinics in Quebec, Canada. Data were collected between 2022 and 2023 from four cases of champions in Quebec and Ontario who had implemented a change using a QI approach. RESULTS: Seven CMO configurations were observed empirically. Findings highlight that a strong coalition of champions, facilitated by cohesion, collaboration, and a shared vision, enhances mobilization. Champions influence QI by prioritizing objectives, reducing perceived effort, and leveraging data to justify change. Their leadership and credibility increase motivation, while their on-site presence helps them anticipate barriers and adapt strategies. Champions initiate small-scale testing to refine interventions and engage peers. Effective communication of results sustains momentum. Ultimately, a champion’s ability to engage the right individuals at the right time, build trust, and provide mentorship determines the success and sustainability of QI initiatives. CONCLUSION: This study underscores the complexity of implementing change in primary care organizations. The refined program theory enhances understanding of how champions contribute to successful change implementation and how their behaviours can be adapted to local contexts to optimize outcomes. These findings support a context-sensitive approach to leveraging champions as facilitators of change.
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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 | 0.065 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".