Determining the Costs of Families’ Support Networks Following a Child’s Cancer Diagnosis
Bibliographic record
Abstract
BACKGROUND: Cancer in children may place considerable economic burden on more than individual family members. The costs incurred to families' support networks (FSNs) have not been previously studied. OBJECTIVES: The study objectives were to (a) identify and determine independent predictors of the direct and time costs incurred by the FSN and (b) explore the impact of these cancer-related costs on the FSN. METHODS: A prospective mixed-methods study was conducted. Representing the FSN, parents recorded the resources consumed and costs incurred during 1 week per month for 3 consecutive months, beginning 1 month following their child's diagnosis. Descriptive statistics, multiple regression modeling, and descriptive qualitative analytical methods were used to analyze the data. RESULTS: In total, 28 fathers and 71 mothers participated. The median total direct and time costs for the 3 months were CAN$154 and $2776, respectively, per FSN. The largest component of direct and time costs was travel and foregone leisure. Direct and time costs were greatest among those parents who identified a support network at baseline. Parents relied on their FSN to "hold the fort," which entailed providing financial support, assuming household chores, maintaining the siblings' routines, and providing cancer-related care. CONCLUSIONS: Families' support networks are confronted with a wide range of direct and time costs, the largest being travel and foregone leisure. IMPLICATIONS FOR PRACTICE: Families' support networks play an important role in mitigating the effects of families' costs. Careful screening of families without an FSN is needed.
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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 | 0.002 | 0.017 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".