Enhancing Cancer Patient Navigation: Lessons from an Evaluation of Navigation Services in Alberta, Canada
Bibliographic record
Abstract
Cancer patient navigation has emerged as a patient-centric intervention enabling equitable cancer care, by mitigating barriers patients encounter throughout their cancer journey. Cancer Care Alberta (CCA) implemented a professional navigation model over a decade ago and commissioned a program evaluation in response to evolving operational demands. The objectives were (1) to better understand the current state of CCA's cancer patient navigation program; (2) to explore the need for other specialized streams; and (3) to provide key recommendations to strengthen and grow the program. A mixed methods approach, including a survey, administrative data, and semi-structured interviews, captured patient-, staff-, and system-level insights. Findings revealed difficulties in identifying complex patients needing navigation, along with inconsistencies regarding intake practices, program awareness, referral pathways, standardized workflows, and a lack of programmatic supports, which contributed to variability in service delivery. A need for enhanced palliative navigation support also emerged. Approximately 25% of surveyed patients reported being unable to access perceived needed support before their first oncology consultation. These findings underscore the importance of early, targeted navigation for equity-deserving populations. Recommendations include harmonizing program structure, refining navigator roles, expanding navigation streams, standardizing processes, and enhancing equity-focused competencies. These findings offer a roadmap with which to improve person-centered 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 | 0.021 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".