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Record W1983361678 · doi:10.1177/0269216310395647

Development and evaluation of a combined story and fact-based educational booklet for patients with multiple brain metastases and their caregivers

2011· article· en· W1983361678 on OpenAlexaff
Chris Kitamura, Andrew D. Chung, Andrea Bezjak, C. Garraway, Michael McLean, Joyce Nyhof‐Young, Rebecca Wong

Bibliographic record

VenuePalliative Medicine · 2011
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsAnxietyReading (process)MedicinePreferencePatient satisfactionClinical psychologyNursingPsychiatry

Abstract

fetched live from OpenAlex

The aim of our study was to design and evaluate the impact on informational satisfaction of a combined story and fact-based educational booklet designed for patients with multiple brain metastases. Phase A evaluated the preference of participants for combined, fact, or story-based writing style. Based on these results, a resource was developed using a combined story and fact-based approach. Patients with newly diagnosed brain metastases and their caregivers read the booklet. Satisfaction was evaluated using the Information Satisfaction Questionnaire and Client Satisfaction Questionnaire. Anxiety was evaluated before and after reading using the State Trait Anxiety Inventory. Ninety-one patients participated in this study. In Phase A, 51% of patients expressed a preference for the combined story and fact-based approach. In phase B, participants expressed high satisfaction for both the informational content and the overall satisfaction towards the pamphlet. The level of anxiety before reading the booklet was lower for caregivers than patients. Anxiety score was increased in the caregiver group after reading the booklet. This was unchanged in the patient group. Both patients and caregivers endorsed the resource. The increase in anxiety in caregivers suggests the tool has been effective in conveying serious prognostic implications.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.158
GPT teacher head0.380
Teacher spread0.221 · 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 designObservational
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

Citations9
Published2011
Admission routes1
Has abstractyes

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