Psychosocial needs of parents with children suffering from cancer in Rwanda: A cross-sectional study
Bibliographic record
Abstract
Background: Each year, approximately 400,000 children between the ages of 0 and 19 are diagnosed with cancer worldwide. The parents of these children experience heightened psychosocial challenges while managing their care throughout the treatment process. Recognizing these needs is essential for creating targeted interventions. This research project aimed to explore the psychosocial needs of parents with children undergoing cancer treatment. Methodology: A community-based cross-sectional study was conducted to gather data from 100 parents or caregivers of children who received cancer treatment at Butaro Cancer Center of Excellence. Data were analyzed using descriptive statistics with SPSS version29. Results: Participants faced significant psychosocial challenges, including managing emotions, such as anxiety, fear, and grief (Mean (M) = 2.2, Standard deviation (SD) = 1.45) and seeking peace of mind (M = 2.1, SD = 1.42), with 28% and 33% reporting extreme effects on emotional support and coping mechanisms. Informational needs were also a concern, as 29% felt uninformed about community resources (M = 2.20, SD = 1.428). Social support needs were notable, particularly the necessity of having someone to stay with the child (M = 2.57, SD = 1.53) was extremely important for 46% of the participants. Financial assistance to cover additional expenses was another challenge, with a mean score of 2.30, reflecting its critical importance. Conclusion: This study offers important insights into the psychosocial needs of parents with children diagnosed with cancer, in Rwanda. Addressing and prioritizing these needs, while creating tailored interventions that target the identified areas, could improve the holistic care significantly and promote interventions to meet the psychosocial needs of parents of children suffering from cancer. Keywords: parents, childhood cancer, psychosocial needs, information needs, financial needs
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".