Navigation Support during Transitions in Care for Persons with Complex Care Needs: A Systematic Review
Bibliographic record
Abstract
Persons with complex care needs that arise due to chronic health conditions, serious illness, or social vulnerability are at increased risk of adverse health outcomes during transitions in care. To inform the development of a best practice guideline, a systematic review was conducted to examine the effect that navigation support has during transitions in care on quality of life, emergency department visits, follow-up visits, patient satisfaction, and readmission rates for persons with complex care needs. Eight databases were searched from 2016 to 2023. Studies were appraised using validated tools and data were extracted and presented narratively. The GRADE approach was used to assess the certainty of the evidence. Seventeen studies were included and the majority focused on transitions from hospital to home. Navigation support was provided for one month to one year following a transition. Results weakly indicate that providing navigation support during transitions in care may increase follow-up visits, reduce readmissions within 30 days, and increase patient satisfaction for persons with complex care needs. There were no important differences for quality of life and emergency department visits within 30 days of a transition. The certainty of the evidence was very low. Providing navigation support during transitions in care may improve outcomes for persons with complex needs; however, there remains uncertainty regarding the effectiveness of this intervention and more high-quality research is needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".