Patient navigators for people with chronic disease: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Individuals with chronic diseases may have difficulty optimizing their health and getting the care they need due to a combination of patient, provider, and health system level barriers. Patient navigator programs, in which trained personnel assess and assist patients in overcoming barriers to care, may improve care and outcomes for patients with chronic disease by providing an alternative approach to conventional information and support resources. METHODS: This systematic review will evaluate the evidence for patient navigator programs, compared to usual care, in patients with chronic disease. We will include RCTs, cluster RCTs, and quasi-randomized RCTs that study the effects of patient navigator programs on clinical outcomes, patient experience, and markers of adherence to care. Studies will be identified by searching MEDLINE, Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), CINAHL, PsycINFO, Social Work Abstracts, and the references of included studies. Two authors will screen titles and abstracts independently. Full texts will be reviewed for relevance and data extraction will be done independently by two authors. Studies will be included if they assess patients of any age with one or more chronic diseases. Outcomes will be categorized into groups characterized by their proximity to mechanism of action of the intervention: patient-level outcomes, intermediate outcomes, and process outcomes. Descriptive data about the elements of the patient navigator intervention will also be collected for potential subgroup analyses. Risk of bias will be assessed using the Effective Practice and Organisation of Care Group (EPOC) risk of bias tool. Data will be analyzed using random effects meta-analysis (relative risk for dichotomous data and mean difference for continuous data), if appropriate. DISCUSSION: A comprehensive review of patient navigator programs, including a summary of the elements of programs that are associated with a successful intervention, does not yet exist. This systematic review will synthesize the evidence of the effect of patient navigator interventions on clinical and patient-oriented outcomes in populations across a comprehensive set of chronic diseases. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42013005857 .
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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 | 0.091 | 0.120 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.023 | 0.031 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.087 | 0.008 |
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".