Mapping Evidence and Constructing an Analytic Framework: An Umbrella Review Summarizing Primary Care Family Physicians’ Experiences With Clinical Integration
Bibliographic record
Abstract
PURPOSE: Strengthening primary care's integration function is a systematic approach to promote integrated care. Understanding the factors influencing the process of integrating care for every patient is crucial for effective intervention planning. The objective of this study was to generate an analytic framework and evidence map of the barriers and facilitators perceived by family physicians (FPs) in clinical integration, a process to coordinate health care services across time, place, discipline, diseases, and patient demographics. METHODS: Using the Joanna Briggs Institute umbrella review methodology, we searched the MEDLINE, Embase, and CINAHL databases, identifying 90 reviews (2010-2022) on primary care FPs and clinical integration. We adopted a best-fit framework approach to group the factors into a customized clinical integration framework, reflecting how a health care system functions. Two evidence maps were created to visualize the reviews' distribution. We validated the framework with another 21 reviews (2022-2024). RESULTS: The analytic framework consisted of 9 themes and 21 subthemes based on 2,891 factors derived from external and internal sources within primary care practices. Several subthemes were common across themes related to individuals (FPs, physicians other than family physicians and allied health providers, patients) and operating units (systems, organizations, practices), highlighting shared elements. The professional theme was the most significant, appearing in 86% of the reviews and including subthemes related to diseases, clinical guidelines, and teamwork. In contrast, themes related to systems, organizations, and practices were reported less frequently (48%, 22%, and 23%). CONCLUSION: The complex interactions among factors, subthemes, and themes elucidate challenges in finding a universal strategy or implementing initiatives. The generated evidence maps indicated knowledge gaps to guide future research work.
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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 | 0.149 | 0.281 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.082 | 0.069 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".