The patient patient: The importance of knowing your navigator
Bibliographic record
Abstract
In Ontario, Diagnostic Assessment Programs (DAPs) have been implemented to improve the quality of care patients receive during the diagnostic phase of the cancer journey. Patient navigators play a critical role in this model by coordinating care and providing information and support to patients and their families. The objectives of this study were 1) to determine whether patient navigation in DAPs is associated with a better patient experience and 2) to examine whether patient navigation in DAPs modifies the effect of wait times and patient volumes on patient experience. Data reflecting patients’ experience within the DAP were collected via survey and an average experience score was calculated for each region. To ascertain the relationship between patient experience, wait times and volumes, correlation coefficients were computed between regional patient experience scores and total regional patient volumes and between regional patient experience score and regional diagnostic wait times. To understand the impact of navigators on the patient experience, the sample was subdivided according to whether or not the respondent reported knowing their navigator. Patients who knew their navigator rated their experience significantly better than those who did not. For those who did not know their navigator, there was an inverse and significant correlation between patient experience scores and wait times; patients in regions with long waits had poorer experience scores overall. Patients who knew their navigator reported consistently good experience regardless of their diagnostic wait. The navigator appears to mitigate the negative impact of longer wait times on experience.
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 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".