Patient Perceptions of a Comprehensive Cancer Navigation Service
Bibliographic record
Abstract
OBJECTIVE: Our aim was to determine the extent to which comprehensive navigation augments the provincial health system for meeting the needs of newly-diagnosed cancer patients (clients). We also assessed reactions of attending physicians to comprehensive navigation. METHODS: Clients who completed navigation as an employee benefit or through membership in an insurance organization were polled to determine whether they needed help beyond that provided by the provincial health system and the extent to which that help was provided by navigation. Exit interviews were analyzed for perceptions of the clients about reactions by their attending physicians to navigation. RESULTS: Of eligible clients, 72% responded. They reported needing help beyond that which the provincial system could provide in 64%-98% of specified areas. Navigation provided help in more than 90% of those cases. Almost all respondents (98%) appreciated having a designated oncology nurse navigator. Family doctors were perceived to be positive or neutral about navigation in 100% of exit interviews. Oncologists were positive or neutral in 92% (p < 0.001 for difference from family doctors). CONCLUSIONS: In many areas, cancer patients need additional help beyond that which the provincial health system can provide. Comprehensive cancer navigation provides that help to a considerable extent. Clients perceived the reactions of attending physicians to comprehensive navigation to be generally supportive or neutral.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".