DEVELOPMENT AND INITIAL PSYCHOMETRIC VALIDATION OF A REAL-TIME PATIENT REPORTED EXPERIENCE MEASURE
Bibliographic record
Abstract
Patient-reported experience measures (PREMs) capture the patient’s view about his or her experience while receiving care across the continuum of care. Your Voice Matters (YVM), a real-time electronic PREM tool, was developed to measure the patient experience in the outpatient cancer setting and to drive quality improvements in the cancer system. This study describes the development and validation of YVM, a real-time electronic PREM tool in cancer services. Cognitive interviewing was conducted with patient and family advisors for both the French (n = 3) and English (n = 5) versions of the YVM tool. YVM was administered through five Regional Cancer Centers (RCCs) between April and August 2015. Shapley value regression used overall experience-dependent variables to determine core items and items eligible for removal from YVM. Exploratory factor analysis was used to determine the underlying factor structure.Internal consistency reliabilities were calculated using Cronbach’s alpha. A total of 557 YVM tools were completed by cancer patients in the treatment phase. Shapley value regression identified five lower scoring items for removal. Exploratory factor analysis showed that a 27-item, five-factor structure reflected the underlying patient experience dimensions in the cancer treatment visit. Cronbach’s alpha of 0.827 for all items suggested good internal consistency. YVM is a validated tool for measuring the experience of cancer patients during the treatment phase through the visit trajectory in real time. YVM will help drive improvements based on patients’ preferences and needs, and will provide robust patient experience data for cancer care delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".