Mapping Evidence of Patients’ Experiences in Integrated Care Settings: A Protocol for a Scoping Review
Bibliographic record
Abstract
INTRODUCTION: Integrated care (IC) models have emerged to address gaps in care for individuals with complex healthcare needs. Although the clinical and cost-effectiveness of IC models are well-established, our understanding of whether IC models facilitate a patient-centred care experience from the patients' perspective is not well understood. This scoping review aims to comprehensively map the literature to provide a broad overview of patients' experiences in IC settings with a focus on the experiences of complex patients with comorbid mental and physical illnesses. It also aims to describe current gaps identified in the literature in our understanding of aspects of care that are often unrecognised. METHODS AND ANALYSIS: Using established scoping review frameworks and guidelines, we will perform a comprehensive search in the following databases: MEDLINE, EMBASE, PsycINFO, CINAHL, AMED and the Cochrane Library to identify relevant studies on patients' experiences in IC models. Grey literature sources and studies bibliographies will also be searched to identify relevant studies and documents. Data will be extracted and summarised using descriptive statistical and qualitative analyses. We will also consult with stakeholders from various backgrounds to enhance the comprehensiveness of this review. ETHICS AND DISSEMINATION: This review requires no ethical approval. Findings from this study will be disseminated through publication in a peer-reviewed journal, clinical conferences and in knowledge translation settings, aiming to improve clinical practice and care delivery.
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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 | 0.128 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.016 |
| Bibliometrics | 0.030 | 0.026 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.051 | 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".