Pilots of Oncology Health Care: A Concept Analysis of the Patient Navigator Role
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To summarize the current scientific literature pertaining to the role of the patient navigator in oncology using the concept analysis framework developed by Walker and Avant. DATA SOURCES: Published research articles, clinical articles, and Internet sources on patient navigator roles and programs. Literature was obtained from CINAHL, PubMed, PsycINFO, the Cochrane Library, and Google Scholar, incorporating reports in English from 1990-2008. DATA SYNTHESIS: Patient navigation has received a plethora of attention as healthcare programs strive to streamline care and address current gaps in service delivery. The literature revealed that the role of the patient navigator remains context-specific and has been filled by a variety of individuals, including nurses, social workers, peer supporters, and lay individuals. CONCLUSIONS: The role of a patient navigator includes removing barriers to care, improving patient outcomes, and ameliorating the overall quality of healthcare delivery. IMPLICATIONS FOR NURSING: By examining the role of the patient navigator depicted in the scientific literature, nurses can gain insight into not only the features of navigation but also the current systematic gaps that call for navigation services. This article examines the numerous functions of a patient navigator and exemplifies the significance of the role in various domains.
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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 | 0.047 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 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".