HELENE HUDSON LECTURESHIP: Developing a provincial cancer patient navigation program utilizing a quality improvement approach Part two: Developing a Navigation Education Framework
Bibliographic record
Abstract
In 2012, the provincial cancer agency in Alberta initiated a provincial quality improvement project to develop, implement, and evaluate a provincial cancer navigation program spanning 15 sites across more than 600,000 square kilometres. This project was selected for two years of funding (April 2012-March 2014) by the Alberta Cancer Foundation (ACF) through an Enhanced Care Grant process (ACF, 2015). A series of articles has been created to capture the essence of this quality improvement (QI) project, the processes that were undertaken, the standards developed, the education framework that guided the orientation of new navigator staff, and the outcomes that were measured. The first article in this series focused on establishing the knowledge base that guided the development of this provincial navigation program and described the methodology undertaken to implement the program across 15 rural and isolated urban cancer care delivery sites (Anderson et al., 2016). This article, the second in the series, delves into the education framework that was developed to guide the competency development and orientation process for the registered nurses who were hired into cancer patient navigator roles and how this framework has evolved to support navigators, as they move from novice to expert practice. The third and final article will explore the outcomes that were achieved through this quality improvement project culminating with a discussion section highlighting key learnings, adaptations made, and next steps underway to broaden the scope and impact of the provincial navigation program.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".