Patient satisfaction with the role of a Clinical Specialist Radiation Therapist in palliative care
Bibliographic record
Abstract
Abstract Aim To examine patient satisfaction with a Clinical Specialist Radiation Therapist (CSRT) in a palliative radiotherapy clinical environment. Materials and methods A one-point dissemination design captured satisfaction scores from patients who did (n=19) and did not (n=14) receive palliative care from the CSRT. The ‘Patient Satisfaction Questionnaire’ included six common questions and four additional questions for patients seen by a CSRT. T-tests compared results from common questions and mean values, standard deviations were also calculated. Results For questions ‘I was told everything that I want to know about my condition’ and ‘I felt that the problem that I came with was sorted out properly’, those who received care from the CSRT scored significantly (p<0·05) higher than those that did not (p=0·033, 0·037). For CSRT-specific questions, 89% of participants felt the experience with the CSRT was excellent, 78% strongly agreed/agreed having a CSRT on the care team was important, and 89% of participants strongly agreed/agreed having a CSRT on the care team was important to patients’ understanding of treatment. Findings Patients receiving care from the CSRT had better understanding of treatment and an excellent experience with the CSRT. This interaction provided more opportunities to address patient questions/concerns, thus alleviating patient anxiety, increasing satisfaction with care, and demonstrating how new roles can develop new models of care within the current healthcare system.
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 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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".