Case management in primary care among frequent users of healthcare services with chronic conditions: protocol of a realist synthesis
Bibliographic record
Abstract
INTRODUCTION: A common reason for frequent use of healthcare services is the complex healthcare needs of individuals suffering from multiple chronic conditions, especially in combination with mental health comorbidities and/or social vulnerability. Frequent users (FUs) of healthcare services are more at risk for disability, loss of quality of life and mortality. Case management (CM) is a promising intervention to improve care integration for FU and to reduce healthcare costs. This review aims to develop a middle-range theory explaining how CM in primary care improves outcomes among FU with chronic conditions, for what types of FU and in what circumstances. METHODS AND ANALYSIS: A realist synthesis (RS) will be conducted between March 2017 and March 2018 to explore the causal mechanisms that underlie CM and how contextual factors influence the link between these causal mechanisms and outcomes. According to RS methodology, five steps will be followed: (1) focusing the scope of the RS; (2) searching for the evidence; (3) appraising the quality of evidence; (4) extracting the data; and (5) synthesising the evidence. Patterns in context-mechanism-outcomes (CMOs) configurations will be identified, within and across identified studies. Analysis of CMO configurations will help confirm, refute, modify or add to the components of our initial rough theory and ultimately produce a refined theory explaining how and why CM interventions in primary care works, in which contexts and for which FU with chronic conditions. ETHICS AND DISSEMINATION: Research ethics is not required for this review, but publication guidelines on RS will be followed. Based on the review findings, we will develop and disseminate messages tailored to various relevant stakeholder groups. These messages will allow the development of material that provides guidance on the design and the implementation of CM in health organisations. TRIAL REGISTRATION NUMBER: Prospero CRD42017057753.
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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 | 0.160 | 0.247 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.071 | 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".