Primary Care Family Physicians’ Experiences with Clinical Integration: A Scoping Review of Qualitative and Mixed Reviews
Bibliographic record
Abstract
Background: Clinical (service) integration in primary care settings describes how healthcare services are coordinated over time across healthcare contexts to meet patient care needs. Promoting integrated care with interventions that lack systematic approaches can unintentionally increase the complexity and fragmentation of the healthcare system. To improve care integration and healthcare service planning, a systematic approach to understanding its many influencing factors is paramount. This phase I study aims to generate a comprehensive map of family physician (FP) perceived factors, across diseases and patient demographics, influencing how services are integrated for patients. Methods: The protocol development is guided by Arksey and O’Malley’s scoping review methodology framework. Keywords and MeSH terms were contributed by a multidisciplinary team of healthcare professionals, researchers, and patient representatives through an iterative discussion. An Information Specialist with clinical research expertise tested keywords and MeSH terms to build a search strategy using MEDLINE. The strategy was then adjusted for the EMBASE and CINAHL databases. All identified articles were imported into Covidence platform, and 228 duplicates were removed. Two independent reviewers screened 1,771 studies by their titles and abstracts. Of them, 264 studies were reviewed in full text against the selection criteria and their references were hand-searched. A total of 91 articles were included, and over 2,800 factors were extracted. The factors are being or will be coded into themes the Clinical Integration Framework, a merging of two existing models identified from literature and modified. Within each theme, similar codes are clustered together as a subtheme. Discussion: The identified factors will help develop a survey in Phase II study to ascertain high impact factors for intervention(s), as well as thematic and evidence gaps to guide future research. The study findings will be shared with stakeholders to promote awareness of clinical integration issues through multiple channels: publications and conferences for researchers and care providers, an executive summary for clinical leaders and policy makers, and social media for the public.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.092 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.023 | 0.026 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| 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".