Informational Stories: A Complementary Strategy for Patients and Caregivers with Brain Metastases
Bibliographic record
Abstract
OBJECTIVE: We compared the efficacy of a story-based writing style with that of a fact-based writing style for educational material on brain metastases. METHODS: Identical informational content on four topics-radiation therapy, side effects, steroid tapering, and palliative care-was constructed into equivalent story-based and fact-based materials. The content and reader preference for style were evaluated using a questionnaire of 20 + 1 items. Cancer patients and caregivers were invited to evaluate the materials. RESULTS: A total of 47 participants completed the questionnaire. The recorded preferences for facts, stories, or both were 42%, 7%, and 51% respectively (p = 0.0004). The fact-based materials were rated superior in providing factual information (for example, discussion of treatment, side effects) and selected general characteristics (clarity of information, for instance). A rating trend suggested that story-based materials were superior in describing "how it feels to have brain metastases" (21/40 fact-based vs. 26/43 story-based) and "how brain metastases affected a spouse" (17/41 fact-based vs. 21/47 story-based), and in being "sensitive to the frustrations of a patient with brain metastases" (25/40 fact-based vs. 30/44 story-based). CONCLUSIONS: Half the participants preferred to read both fact-based and story-based materials. A combined story-based and fact-based educational resource may be more effective in conveying sensitive information and should be further investigated.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".