Primary care family physicians’ experiences with clinical integration in qualitative and mixed reviews: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Clinical (service) integration in primary care settings describes how comprehensive care is coordinated by family physicians (FPs) over time across healthcare contexts to meet patient care needs. To improve care integration and healthcare service planning, a systematic approach to understanding its numerous influencing factors is paramount. The objective of this study is to generate a comprehensive map of FP-perceived factors influencing clinical integration across diseases and patient demographics. METHODS AND ANALYSIS: We developed the protocol with the guidance of the Joanna Briggs Institute systematic review methodology framework. An information specialist built search strategies for MEDLINE, EMBASE and CINAHL databases using keywords and MeSH terms iteratively collected from a multidisciplinary team. Two reviewers will work independently throughout the study process, from article selection to data analysis. The identified records will be screened by title and abstract and reviewed in the full text against the criteria: FP in primary care (population), clinical integration (concept) and qualitative and mixed reviews published in 2011-2021 (context). We will first describe the characteristics of the review studies. Then, we will extract qualitative, FP-perceived factors and group them by content similarities, such as patient factors. Lastly, we will describe the types of extracted factors using a custom framework. ETHICS AND DISSEMINATION: Ethics approval is not required for a systematic review. The identified factors will help generate an item bank for a survey that will be developed in the Phase II study to ascertain high-impact factors for intervention(s), as well as evidence gaps to guide future research. We will share the study findings with various knowledge users 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.
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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 | 0.171 | 0.131 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.064 | 0.011 |
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".