Patient perceptions of advanced practice radiation therapists prescribing medication in radiation therapy
Bibliographic record
Abstract
INTRODUCTION: The purpose of this study was to determine patient perceptions of an advanced practice radiation therapist (APRT) prescribing medication for radiation therapy treatment-related side effects. By comprehending patient perceptions, it is important to implement change in order to improve patients' quality of life. METHODS: A literature review was conducted on advanced practice (AP) roles in Canada and world-wide; the roles searched were: APRT, nurse practitioner and pharmacist. The search focused on evidence demonstrating improvements made to patient care due to the implementation of these roles. Based on this review and input from a team of experts a qualitative semi-structured interview survey was designed, and pilot tested. The survey consisted of five open-ended questions, which were designed to determine patient satisfaction of an APRT prescribing medication over the course of their radiation therapy treatments. Patients undergoing head and neck radiation therapy treatments at a large, academic cancer centre were invited to participate. Six patients who had a head and neck APRT involved in their treatment were interviewed. A comprehensive thematic analysis was then conducted using the transcripts created from these interviews, which was followed by two independent blinded analyses to ensure validity of the results. DISCUSSION: The thematic analysis produced four salient themes which were: side effect management, care provided by the APRT in comparison to other healthcare workers, patients' access to care, and overall patient satisfaction. Common medications for head and neck radiation therapy treatment related side effects were discussed and these were: Magic Mouthwash, Xylocaine, Nystatin, Benadryl, Advil, Tylenol, Dexamethasone, Tantum, Biotene, Mucaine, Flamazine, Hydrocortisone, Ondansetron, Senokot, and narcotics. CONCLUSION: This study was valuable to understand patient experiences and provide evidence to change processes in order to improve quality of patient centered care. The study revealed that although patients were happy with the process of prescribing medication, they all agreed that having an advanced practice radiation therapist prescribe would improve care. Patient responses further demonstrated the need for future research in regards to side effect management as a whole by APRTs as well as how role clarification can impact patient perceptions of APRTs.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".