The nurse pivot-navigator associated with more positive cancer care experiences and higher patient satisfaction
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Growing evidence indicates that the nurse navigator-pivot (NN), is key to optimizing care processes and outcomes. However, large scale studies are needed to examine how patients exposed to NNs (as opposed to non-NN) differentially perceived their cancer care experiences. METHOD: Participants (N = 2,858) treated for cancer in the last six months at university-affiliated cancer centres in Montréal, Québec, completed the Ambulatory Oncology Patient Satisfaction Survey (AOPSS). RESULTS: Cancer care experiences and satisfaction were significantly higher in the NN group (n = 2,003) for all six care domains (Ds from 3.32 to 8.95) and all four nursing functions (Ds from 5.64 to 10.39) when compared to the non-NN group (n = 855). DISCUSSION: The NN role is significantly related to enhanced cancer care experiences and higher patient satisfaction. Future research should explore potential causal effects between NNs and care processes, as well as patient outcomes.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".