Patient reported experience in a radiation oncology department
Bibliographic record
Abstract
Understanding patient experience is essential to providing high quality, person-centered care. A real-time baseline cross-sectional study was completed to identify gaps in patient experience that can be targeted for quality improvement (QI). This study is part of PROSE (Person-centered Radiation Oncology Service Enhancement), a QI initiative developed to improve patient experience at a tertiary cancer centre Radiation Oncology (RO) Department. Data was collected using the Your Voice Matters (YVM) questionnaire. The YVM captures information on the patient’s last visit, and questions are organized based on dimensions of person-centered care. Recruitment occurred between May and August 2019 in the radiation department. Consecutive patients during the study period were approached to complete the YVM either in reference to their initial consultation or previous treatment appointment. Percent positive scores were calculated for quantitative data and a content analysis was completed for open-text data. Of 512 patients approached, a total of 400 patients participated across tumors groups. Overall, patients highly endorsed positive experiences with feeling respected by their healthcare provider. Contacting the clinic, emotional support and wait times were rated as the least positive components of experience across appointment types and tumors groups. The Lung tumour group demonstrated worse experiences across all domains during treatments compared to all other tumour groups. Gaps and differences in patient experience were demonstrated across appointment types and tumour groups. This study provides direction to effectively develop and implement QI work aimed at improving patient experience. Experience Framework This article is associated with the Policy & Measurement lens of The Beryl Institute Experience Framework. (https://www.theberylinstitute.org/ExperienceFramework). Access other PXJ articles related to this lens. Access other resources related to this lens.
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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 | 0.004 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".