Evaluation of Patient Satisfaction: Radiation Therapy Services for Chinese Patients at the British Columbia Cancer Agency – Vancouver Centre
Bibliographic record
Abstract
Abstract Background: Patient satisfaction surveys conducted in English exclude respondents who are not proficient in the English language. This makes it difficult to assess whether health care services provided are culturally appropriate. This study aims to evaluate the level of satisfaction for Chinese speaking patients who received radiation treatments at the British Columbia Cancer Agency, Vancouver Centre in Canada. Patients and Methods: Chinese patients were given a translated patient satisfaction survey on a voluntary basis to complete at the end of treatment. Contingency table analysis using the Pearson chi-square test or Fisher’s exact test was performed at 5% significance level for all analyses. Logistic regression analysis was conducted to investigate whether complete satisfaction with an aspect of care influenced overall satisfaction with services provided by the RT team. Results: The level of satisfaction in Chinese speaking patients was lower compared to English speaking patients. The results from the Chinese survey also identified the importance of treatment patients with courtesy and providing them with a pleasant wait area. Conclusions: Despite a language barrier, Chinese speaking patients still contributed to improvement initiatives at the Vancouver Centre. Efforts to ensure a culturally appropriate environment and provision of services include recruitment of staff members who reflect the cultural diversity of the community serviced, use of interpreter services or bilingual health providers for clients, use of linguistically appropriately education materials, and health care settings that is pleasant and respects the cultural diversity of the population serviced. This assessment provided a better understanding of whether services at the Vancouver Centre were culture appropriate.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.005 | 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".