Investigating and comparing the patients’ and staff's perspectives on the usefulness of a head and neck radiotherapy patient education booklet
Bibliographic record
Abstract
Abstract Introduction Printed patient education material enhances verbal patient teaching. ‘Starting radiation therapy: helpful tips for patients with head and neck cancer’ is a booklet that facilitates head and neck (H&N) cancer patients’ orientation to the study hospital. This study examined and compared patients’ and staff's opinion on the distribution and usefulness of this booklet. Methods Patients starting radiotherapy treatment to their H&N cancer, and staff involved in their care, were recruited. A survey was designed to collect responses from both cohorts. Results Of the patients, 94% received the booklet before their first radiotherapy treatment. Of the staff, 67% referred to this booklet during patient education. Most patients (98%) found that the booklet increased their awareness of hospital and community services. Both groups indicated list of services and telephone number to be the most useful chapter. The staff suggested having this booklet available in different languages. Conclusion This booklet was useful as an orientation tool for the patients to navigate the hospital system. Patients and staff have similar opinion regarding the most useful sections in the booklet. Further studies needs to be conducted to validate the need of having this booklet available in other languages.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".