HELENE HUDSON LECTURESHIP Developing a provincial cancer patient navigation program utilizing a quality improvement approach Part one: Designing and implementing
Bibliographic record
Abstract
In 2012, the provincial cancer agency in Alberta initiated a provincial quality improvement project to develop, implement, and evaluate a provincial navigation program spanning 15 sites across over 600,000 square kilometres. This project was selected for two years of funding (April, 2012–March 31, 2014) by the Alberta Cancer Foundation (ACF) through an Enhanced Care Grant process. A series of articles has been created to capture the essence of this quality improvement project, the processes that were undertaken, the standards developed, the educational framework that guided the orientation of new navigator staff, and the outcomes that were measured. This first article in the series focuses on establishing the knowledge base that guided the development of this provincial navigation program and describing the methodology undertaken to implement the program across 15 rural and isolated urban cancer care delivery sites. The second article in this series will delve into the educational framework that was developed to guide the competency development and orientation process for the registered nurses who were hired into the newly developed cancer patient navigator roles. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".