Nutrition education and cooking workshops for families of children with cancer: a feasibility study
Bibliographic record
Abstract
Abstract Background Changes in food intake are common in children with cancer and are often caused by nausea and perturbations in sense of taste. The VIE (Valorization, Implication, Education) study proposes family-based nutrition and cooking education workshops during childhood cancer treatments. Process evaluation during implementation allows to assess if the intervention was delivered as planned and to determine its barriers and facilitators. The study objective was to describe the implementation process of a nutrition education and cooking workshop program for families of children actively treated for cancer in a non-randomized non-controlled feasibility study. Methods Six open-to-all in-hospital workshops were offered on a weekly basis during a one-year implementation phase. We collected qualitative and quantitative data using field notes and activity reports completed by the registered dietician facilitator; surveys and questionnaires fulfilled by the workshop participants and by the families enrolled in the VIE study. Field notes were used to collect only qualitative data. Survey respondents ( n = 26) were mostly mothers ( n = 19, 73%). Children’s mean age was 7.80 (± 4.99) years and the mean time since diagnosis was 7.98 (± 0.81) months. Qualitative data were codified using hybrid content analysis. The first deductive analysis was based on the Steckler & Linnan concepts. Subthemes were then identified inductively. Quantitative data were presented with descriptive statistics. Results Workshop attendance was low (17 participants over 1 year) and 71% of the planned workshops were cancelled due to lack of participants. The principal barriers to participation referred the child’s medical condition, parental presence required at the child’s bedside and challenges related to logistics and time management. The level of interest in the topics addressed was found high or very high for 92% of the participants. The themes that were perceived as the most useful by parents were related to the child’s specific medical condition. Conclusions Despite high interest, workshops delivered in a face-to-face format were poorly feasible in our sample population. This supports the need to develop educational programs in pediatric oncology using strategies and delivery formats that address the major barriers for participation encountered by families.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".