Definition and improvement of the concept and tools of a psychosocial intervention program for parents in pediatric oncology: a mixed-methods feasibility study conducted with parents and healthcare professionals
Bibliographic record
Abstract
BACKGROUND: Studies have shown that supporting parents in pediatric oncology reduces family distress following a cancer diagnosis. Manualized programs for parents have therefore been developed to reduce family distress. However, these programs have limitations that need to be improved, such as better defining programs' procedures, developing interventions focusing on parents' conjugal relationship, conducting rigorous evaluations of implementation, and proposing adaptations to various cultural dimensions. According to the Obesity-Related Behavioral Intervention Trials (ORBIT) model for the development of behavioral intervention, we improved these limitations and developed TAKING BACK CONTROL TOGETHER, a six in-person intervention sessions to support parents of children with cancer by taking the active components of two programs: Bright IDEAS and SCCIP. Referring to the redesign phase of the ORBIT model, this study aims to refine the definition of this program's design by interviewing parents and healthcare professionals. METHODS: In order to refine the program, we used a sequential mixed-methods study. Parents and healthcare professionals first completed questionnaires assessing the program, and then discussed its limitations, benefits, and areas for improvement in group and/or individual interviews. We performed a descriptive thematic content analysis of the qualitative data from the open-ended questions (questionnaires and interviews) with NVivo 11 to categorize recommendations for the program refinement. RESULTS: The results showed that components seemed pertinent to final users. The main areas needing improvement were the level of complexity and understandability of the parent manual, the possibility to choose the place and time of the intervention, and the lack of ethnic/cultural diversity. Changes to the program were made accordingly. CONCLUSIONS: It is necessary to include end-users when developing complex intervention programs designed for vulnerable populations and sensitive clinical contexts. Following the present refinement, we now have a treatment package, which is safe and acceptable for the target population and has a better chance of yielding a clinically significant benefit for users in a future pilot study.
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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 | 0.046 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".