Assessing overall patient satisfaction among cancer patients: A radiotherapy survey.
Bibliographic record
Abstract
e19249 Background: Healthcare providers (HCPs) strive to maximize the experience for cancer patients. Published reports suggest a variety of characteristics that are considered important. We decided to survey our patients undergoing radiotherapy to determine what they considered desirable traits and characteristics. Methods: An ethics approved 35-item patient satisfaction survey evaluating respondent experience was developed by an interdisciplinary team of HCPs working in the radiation medicine program. It was an anonymous, voluntary, paper-based survey for self-completion. It evaluated a variety of domains with respect to the quality of care patients received, and was administered to patients undergoing radiotherapy. Results: A total of 199 patients completed the survey. The median age was 68, with approximately 54% women and 45% men (1% unreported). Most patients (85%) had been diagnosed with their cancer within the previous year, and the commonly reported malignancies (61%) were breast, prostate and lung cancers. Almost all (95%) “agreed" or "strongly agreed" about the importance of physicians being sensitive and compassionate. Over 90% felt they received adequate explanations about their treatment, and had their questions answered. The vast majority (93%) felt included in the decision-making process. They reported the 5 most important qualities among the HCPs as follows (in descending order): knowledge, kindness, honesty (answering questions/giving information), good communicator and cheerful attitude. Most (>70%) reported feeling connected with their HCPs. Although overall satisfaction was high, there were areas for improvement identified. These included patients being offered future appointments to discuss their diagnosis and treatment, receiving information about clinical trials and other treatment options, and being given contact information for psychosocial and community resources. Also, HCPs tended to focus mainly on the physical needs of patients and to a lesser degree on their emotional needs, but spiritual and cultural needs were not routinely addressed (<10%). Conclusions: Reassuringly, cancer patients receiving radiation report high rates of satisfaction across many aspects of their care. The qualities most appreciated serve as a reminder to clinicians that their role is more than just that of a medical expert. These findings also reinforce the different aspects of holistic care that can be improved.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".