Child’s symptom burden and depressive symptoms among caregivers of children with cancers: an argument for early integration of pediatric palliative care
Bibliographic record
Abstract
BACKGROUND: Childhood cancers evoke various emotional reactions in caregivers which can impair their well-being and roles. Little is known about caregiving and which cancer-associated factors are related to caregiver's depression in resource-restricted settings. We sought to investigate if child's symptom burden is related to depressive symptoms in caregivers. METHODS: Seventy-two caregivers and children with cancers were administered questionnaires to elicit socio-demographic and disease-related data. Subsequently, the child's symptoms were profiled with Memorial Symptom Assessment Scale (MSAS 7-12); while screening for depressive symptoms in caregivers was done using the Center for Epidemiologic Studies Depression Scale-Revised (CES-DR). RESULTS: All the caregivers were parents, and largely mothers (83.7%). The mean ages of caregivers and children were 39±2 and 10±2 years respectively. Majority of caregivers (90.3%) were either ignorant or attributed spiritual causation to the cancers. The common symptoms with prevalence >50% in the children included pain, nausea, worry, and lack of energy (LE); symptoms' prevalence ranged from LE (68%) to itching (32%). Approximately, one-third each of the children reported hair and weight loss which were considered 'unusual' in the design of MSAS 7-12. The symptoms showed variability in distress, frequency and intensity. In particular, pain, lack of appetite (LA) and feeling sad (FS) were reported as most burdensome in >50% of the children. More than one-third of caregivers (38.2%) screened positive for significant depressive symptoms. The global symptom burden (r=0.58) and individual symptom correlated positively with depressive symptoms in caregivers (P<0.05). CONCLUSIONS: Our findings suggest the need for improved awareness creation on childhood cancers to obviate late presentations and poor access to care. Again, early integration of pediatric palliative care in childhood cancer care to ensure symptom management as well as its extended benefits on caregivers' wellbeing is desirable. The pattern of certain "unusual" symptoms in children with cancer in our work suggests the need to put into consideration 'novel' symptoms which were not captured in existing instruments. Future research on supportive care in pediatric cancers is indicated.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".