Development and evaluation of a combined story and fact-based educational booklet for patients with multiple brain metastases and their caregivers
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".