Patient navigation: State of the art or is it science?
Bibliographic record
Abstract
First implemented in 1990, patient navigation interventions are emerging today as an approach to reduce cancer disparities. However, there is lack of consensus about how patient navigation is defined, what patient navigators do, and what their qualifications should be. Little is known about the efficacy and cost-effectiveness of patient navigation. For this review, the authors conducted a qualitative synthesis of published literature on cancer patient navigation. By using the keywords 'navigator' or 'navigation' and 'cancer,' 45 articles were identified in the PubMed database and from reference searches that were published or in press through October 2007. Sixteen studies provided data on the efficacy of navigation in improving timeliness and receipt of cancer screening, diagnostic follow-up care, and treatment. Patient navigation services were defined and differentiated from other outreach services. Overall, there was evidence of some degree of efficacy for patient navigation in increasing participation in cancer screening and adherence to diagnostic follow-up care after the detection of an abnormality. The reported increases in screening ranged from 10.8% to 17.1%, and increases in adherence to diagnostic follow-up care ranged from 21% to 29.2% compared with control patients. There was less evidence regarding the efficacy of patient navigation in reducing either late-stage cancer diagnosis or delays in the initiation of cancer treatment or improving outcomes during cancer survivorship. There were methodological limitations in most studies, such as a lack of control groups, small sample sizes, and contamination with other interventions. Although cancer-related patient navigation interventions are being adopted increasingly across the United States and Canada, further research will be necessary to evaluate their efficacy and cost-effectiveness in improving cancer 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 | 0.040 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".