Navigation roles support chronically ill older adults through healthcare transitions: a systematic review of the literature
Bibliographic record
Abstract
Transitions between various healthcare services are potential points for fragmented care and can be confusing and complicated for patients, formal and informal caregivers. These challenges are compounded for older adults with chronic disease, as they receive care from many providers in multiple care settings. System navigation has been suggested as an innovative strategy to address these challenges. While a number of navigation models have been developed, there is a lack of consensus on the desired characteristics and effectiveness of this role. We conducted a systematic literature review to describe existing navigator models relevant to chronic disease management for older adults and to investigate the potential impact of each model. Relevant literature was identified using five electronic databases - Medline, CINAHL, the Cochrane database, Embase and PsycINFO between January 1999 and April 2011. Following a recommended process for health services research literature reviews, exclusion and inclusion criteria were applied to retrieved articles; 15 articles documenting nine discrete studies were selected. This review suggests that the role of a navigator for the chronically ill older person is a relatively new one. It provides some evidence that integrated and coordinated care guided by a navigator, using a variety of interventions such as care plans and treatment goals, is beneficial for chronically ill older adults transitioning across care settings. There is a need to further clarify and standardise the definition of navigation, as well as a need for additional research to assess the effectiveness and cost of different approaches to the health system.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".