Navigation delivery models and roles of navigators in primary care: a scoping literature review
Bibliographic record
Abstract
BACKGROUND: Systems navigation provided by individuals or teams is emerging as a strategy to reduce barriers to care. Complex clients with health and social support needs in primary care experience fragmentation and gaps in service delivery. There is great diversity in the design of navigation and a lack of consensus on navigation roles and models in primary care. METHODS: We conducted a scoping literature review following established methods to explore the existing evidence on system navigation in primary care. To be included, studies had to be published in English between 1990 and 2013, and include a navigator or navigation process in a primary care setting that involves the community- based social services beyond the health care system. RESULTS: We included 34 papers in our review, most of which were descriptive papers, and the majority originated in the US. Most of the studies involved studies of individual navigators (lay person or nurse) and were developed to meet the needs of specific patient populations. We make an important contribution to the literature by highlighting navigation models that address both health and social service navigation. The emergence and development of system navigation signals an important shift in the recognition that health care and social care are inextricably linked especially to address the social determinants of health. CONCLUSIONS: There is a high degree of variance in the literature, but descriptive studies can inform further innovation and development of navigation interventions in primary care.
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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 | 0.028 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.021 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".