Patient navigation in cancer diagnostics: Providing information and support for more effective care.
Bibliographic record
Abstract
5 Background: Patients frequently have a difficult time finding their way through the process of diagnostic testing for cancer; the uncertainty is stressful and anxiety can impede effective communication and decision-making. Patient navigation is an ideal approach to supporting patients during the diagnostic phase of cancer care. Methods: Between 2010 and 2011, 14 Patient Navigators were trained and introduced into Diagnostic Assessment Programs (DAPs) across Ontario. DAPs are designed to provide coordination and supportive care to patients undergoing diagnostic testing and assessment. A patient survey served to evaluate the impact of patient navigation on the patient experience. Standardized tools were introduced to measure symptom severity and symptom management. Interviews were done to gauge provider and team perspectives on the role. Results: During the pilot, patient symptoms improved in the areas of well-being, tiredness, anxiety and shortness of breath, each by 30% or more. The patient survey indicated that through the program, patients got the information and support they needed; 91% of patients surveyed said that they were either satisfied or very satisfied with their experience with the patient navigator. The navigation pilot sites saw their diagnostic wait times fall by 55%. The Patient Navigators have found their work to be highly satisfying and rewarding, enjoying the opportunity it provides to use their full scope of practice in a multidisciplinary environment. Physicians expressed a high degree of satisfaction with the support provided by the Patient Navigators, reporting that patients were better prepared and informed when they arrived for their clinic appointments. Conclusions: This work has shown that patient navigation enhances the experience of patients as they move along the diagnostic continuum. Patient Navigators assist patients with managing their physical and psychosocial symptoms and play a critical role in providing the information and support patients need during a time that is full of uncertainty and distress. The diagnostic phase is a prime example of where navigation can have an impact on both the quality of patient care and the effectiveness of the health care team.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".