A Multi-Site Refinement Study of Taking Back Control Together, an Intervention to Support Parents Confronted with Childhood Cancer
Bibliographic record
Abstract
(TBCT), a manualized six-session program targeting individual problem-solving skills and dyadic coping. The current study aimed to refine TBCT for future uptake across different sites. We invited potential interventionists and local stakeholders from three pediatric oncology centers (CHU Sainte-Justine, CHU de Sherbrooke, and CHU de Québec) to join the refinement team. The final working team comprised 26 professionals, including social workers, psychologists, researchers, coordinators, and parent-partners. The study included eight 50- to 90-min discussion sessions designed to stimulate conversation and facilitate the exchange of ideas and perspectives. We used framework analysis to identify and describe patterns within the qualitative data. The data were organized into three categories: (1) intervention description, which addresses changes in personnel, modes of delivery, and tailoring to accommodate different family structures; (2) content modifications, which include language simplification and visual enhancements; and (3) factors influencing TBCT's future uptake, such as accessibility, participant satisfaction, clinician compensation, and flexibility in program delivery. The direct output of this research is a refined program with an updated manual, tools, and format adapted for use in different sites.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".