Parents' Experiences in Decision Making With Childhood Cancer Clinical Trials
Bibliographic record
Abstract
Childhood cancer requires families to deal with many stressors, including decision making in terms of their child's treatment. Adding to the stress of families is that most children participate in clinical research trials. Minimal research has been done to explore parents' decisions related to involving their child in childhood cancer clinical trials. Especially missing is a description of Canadian parents' perspectives. This article describes a qualitative study that sought to understand Canadian parents' participation in decisions about childhood cancer clinical trials. Person-centered, individual, open-ended interviews were conducted with 31 parents of children with cancer. The parents ranged in age between 27 and 51 years. Data analyzed by the constant comparative method revealed that parents found their participation in decisions about childhood cancer clinical trials as a difficult and extraordinary experience that included 6 themes: (1) living a surreal event, (2) wanting the best for my child, (3) helping future families of children with cancer, (4) coming to terms with my decision, (5) making one decision among many, and (6) experiencing a sense of trust. This study indicates that parents need more support not only during the initial decision-making period but also throughout the entire time their child is enrolled in a clinical trial.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".