Examining patient and visit characteristics associated with the cancer patient experience.
Bibliographic record
Abstract
204 Background: Your Voice Matters (YVM) is a 28-item patient experience survey for adult cancer outpatients undergoing treatment in Ontario.The purpose was to examine patient and visit characteristics associated with YVM responses. Methods: YVM was administered to eligible patients (n=8,704) at 14 centres in 2017.Respondents answered items on a five-point Likert scale (4 or 5 = positive response). A multivariable logistic regression (MLR) was undertaken for each YVM item.Variables (reference group) included: disease site (heamatology), age group (65+), sex (female), rurality (urban), last visit type (consult), income quintile(highest) and immigration tercile (lowest). Significant results ( p<.05) vary by MLR model. Therefore a p-value range is provided for all significant variables for all MLR models. Results: Patients were more likely to have a less positive experience if they had central nervous system ( p =.000-.013), gastrointestinal ( p =.004-.026), head and neck ( p =.002-.025), or lung cancer ( p =.017-.049); were between the ages of 18-39 years ( p = <.0001-.048); were female ( p =.0001-.022); received chemotherapy( p =.0001-.047); lived in a rural location ( p =.042); from a mid-( p =.027-.045) and mid-high income quartile ( p =.021); and in the high- ( p =.002-.043) and middle-immigrant terciles ( p =.028-.047), when compared to the reference group. Patient were more likely to have a more positive experience if they had skin cancer ( p =.004-.032); were between the ages of 40-64 years ( p =.003-.033); receiving radiation ( p =.0001-.041) or a minor procedure ( p =.015); and were from the mid-low ( p =.003-.042) and low income quartile ( p =.001-.032), when compared to the reference group. Conclusions: The cancer patient experience varies by patient and visit characteristics. Future initiatives should examine these characteristics to better understand their populations to create a more tailored approaches to cancer care.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".