Understanding disparities and organisational variation in cancer patient experience: Lessons from the English National Cancer Patient Experience survey.
Bibliographic record
Abstract
181 Background: Surveys of the experience of cancer patients are currently being introduced in several countries, including the U.S. and Canada. Insights to inform the development and use of such surveys can be acquired from the English Cancer Patient Experience Survey programme. Methods: Each of the three national surveys (2010, 2011/12 and 2012/13) had a responder sample of ~70,000 patients (response rate 64-67%) and included about 60 evaluative questions on all domains of experience, from cancer diagnosis to care after hospital treatment. Anonymous data were analysed, using appropriate logistic regression models for positive/negative experience outcomes, based on public reporting conventions. We illustrate a research programme using the survey data focusing on two questions. 1. Who are the patients at greater risk of a negative experience? 2. Does case-mix explain apparent variation between hospitals? Results: There was evidence of socio-demographic variation in cancer patient experience, consistently across questions: younger and very old patients, ethnic minorities, and women reported poorer experience. Regarding variation between patients with 36 common and rarer tumours, those with hepato-biliary and thyroid cancers were most likely to be less satisfied with their overall experience, whereas patients with breast cancer were least likely (top-to-bottom odds ratio 3.7, p<0.0001). There were disparities in experience between patients treated by the same specialty for 5/9 services (p<0.0001). For example, patients with ovarian, multiple myeloma, anal and renal cancer reported worse experiences than patients with other gynaecological, haematological and urological malignancies, respectively. There was high concordance between crude and adjusted ranks of hospital scores (median Kendall's τ=0.84; interquartile range: 0.82-0.88). Conclusions: Initiatives to improve cancer patient experience may be suitably targeted on patients at higher risk of poorer experience. Evidence about disparities in experience and the effect of case-mix provide useful priors to motivate similar inquiries in other healthcare settings.
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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 | 0.022 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".