Demographic, socioeconomic, and clinical factors associated with oncology patient experience in the Ontario cancer system.
Bibliographic record
Abstract
160 Background: Your Voice Matters (YVM) is an electronic real time Patient Reported Experience Measure (PREM) collected on all adult patients accessing cancer services in Ontario by Cancer Care Ontario/Ontario Health. To our knowledge, this is the largest oncology PREM dataset worldwide. A total of 25 Items are assessed including care coordination, wait times, access to care and satisfaction with healthcare providers. We attempted to identify demographic, socioeconomic and disease factors that predict for a positive patient experience. Methods: Responses were collated from 18 individual cancer centers between Jan 2017 and Dec 2019. Each item was dichotomized into positive/negative experiences. Multivariable logistic regression was constructed for each of the 25 items. Results: Demographics are described in table. Males (OR=1.11, p=0.0032), genitourinary patients (OR=1.30, p=0.0031) and those receiving radiation (OR=1.39, p=<0.0001) were more likely to have a positive experience. Patients aged 18-39 (OR=0.74, p=<0.0001), receiving chemotherapy (OR=0.76, p=0.0002), and with central nervous system or lung cancer (OR= 0.56, p=<0.001; OR=0.79 p=0.0059, respectively) were more likely to have a negative experience. Lowest income patients were more likely to have a negative experience with healthcare providers (OR=0.83, p=0.0119) and patients from the highest immigration areas had a worse quality experience (OR=0.82, p=0.0029). Conclusions: Age, sex, disease site, visit type, income and immigration status significantly influence patients cancer care experience. This information helps promote equity and the use of PREM data to improve cancer care delivery. [Table: see text]
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".