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Record W2317196581 · doi:10.1017/s1460396913000289

Investigating and comparing the patients’ and staff's perspectives on the usefulness of a head and neck radiotherapy patient education booklet

2013· article· en· W2317196581 on OpenAlexaff
Kitty Chan, Caroline Davey

Bibliographic record

VenueJournal of Radiotherapy in Practice · 2013
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineHead and neck cancerRadiation TherapistFamily medicineRadiation therapyHead and neckNursingPhysical therapySurgery

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.466

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.115
GPT teacher head0.399
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2013
Admission routes1
Has abstractyes

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