Parent Perspectives on Information-seeking, Trustworthiness, and Decision-making in High-risk Neuroblastoma
Bibliographic record
Abstract
BACKGROUND: This study explores how parents of children with high-risk neuroblastoma incorporate information from multiple sources into treatment decision-making for their children as they evaluate the trustworthiness of the sources. METHODS: Following ethics board approval, parents of children with high-risk neuroblastoma were recruited through purposive sampling from a tertiary care pediatric oncology program in Vancouver, BC, Canada. Participants completed an in-depth, semistructured interview with a study member. The qualitative descriptive methodology was utilized to code interview transcripts and identify emergent themes. RESULTS: Nine parents of children with high-risk neuroblastoma during upfront therapy (n=4) or treatment of refractory disease (n=5) were included. Despite almost universal access of web-based information, parents acknowledged distrust in the reliability and consistency of these sources. Open communication between parents and physicians about sources of information outside the clinic and access to regulated, accurate information is highly valued. The impact on the quality of life and the costs, both financial and personal, of travel are key factors in decision-making. DISCUSSION: Health care providers shoulder an immense responsibility to augment and contextualize information available about high-risk neuroblastoma for parents to maximize benefit in decision-making. Health care providers should guide access to accurate, evidence-based resources that can be monitored and continuously updated.
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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 | 0.020 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".