Identifying Parental Needs When Caring for a Child or Adolescent With Cancer: Participatory Design Study
Bibliographic record
Abstract
Background Survival possibilities among children and adolescents (referred to as “children”) with cancer have increased, resulting in more children and parents living with sequelae and psychological distress, respectively. Childhood cancer treatment is intensive and long term and affects the everyday life and functioning of the whole family, including daily and social life restrictions and hospitalizations, which force the parents to split up between the hospital and home to care for both the ill child and siblings and manage their jobs and homelife routines. Adequate parental coping strategies are essential for family adjustment and the child’s coping ability. Parenting a child with cancer, however, is challenging and stressful. Objective This study aimed to identify parental needs when caring for a child with cancer. Methods Using a participatory design approach, we generated data through field studies and semistructured interviews with parents of children receiving treatment at Odense or Aarhus University Hospital and focus group interviews with parents of children who have completed treatment within the past 2 years. Results The parents felt trapped in an endless, emotional roller-coaster ride of fear, hope, grief, and mental and emotional exhaustion and had very limited resources for self-care. The presence of support from peers, health care providers, and relatives was a facilitator of successfully coping with the illness. In contrast, full-time isolation in the hospital, due to COVID-19 restrictions, was a barrier and linked with feelings of loneliness. Loneliness was also experienced as navigating difficult emotions when responding differently from the spouse or not feeling understood by relatives. Conclusions Parents of children with cancer face many stressors that may become a psychological burden. New innovative approaches to provide psychosocial support, taking into account that the families are periodically isolated, are needed. For this goal, technological solutions may be useful and should be the focal point of this study’s experimental design phase.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".