Primary care engagement in health system change: a scoping review of common barriers and effective strategies
Bibliographic record
Abstract
BACKGROUND: The complexity of health systems necessitates coordination between a multitude of stakeholders to enact meaningful change. Primary care physicians are a crucial partner to engage, as their investment and participation are critical to the success of any system-level initiative. The aim of this scoping review is to identify common barriers and effective strategies when engaging primary care physicians in designing and implementing health system change. METHODS: A scoping review was performed. A literature search was performed in March 2020 using five databases. 668 unique articles were identified and underwent a title and abstract review. 23 articles met criteria for full text review and 10 met final inclusion criteria. A backward citation analysis identified two articles. 12 articles underwent data extraction and thematic analysis. RESULTS: Several barriers to engagement were identified including a lack of trust between primary care physicians and decision-makers, strong professional physician identity, clinically irrelevant and complex proposals, and a lack of capacity and supports. Described strategies to overcome these barriers included building trust and relationships, contextual engagement strategies, working with physician leadership, enabling open and intentional communication channels, designing clinically relevant and straightforward initiatives, and considering financial incentives. CONCLUSIONS: Barriers to primary care engagement should be addressed with contextually designed strategies and a focus on relationship building, collaborative efforts, and implementing relevant and feasible initiatives. Further research should explore how to best develop relationships with primary care, working with collective voices of primary care physicians, and to better understanding the impact of financial incentives on engagement.
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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 | 0.081 | 0.213 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.028 | 0.028 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".