Shifting Priorities for the Survival of My Child
Bibliographic record
Abstract
BACKGROUND: Parents are confronted with a range of direct costs and intense caregiving demands following their child's cancer diagnosis, which may potentially threaten the financial stability of the family. OBJECTIVE: The aims of this study were to explore the financial impact of a new childhood cancer diagnosis on families and understand the strategies families use to manage these financial impacts. METHODS: As part of the mixed-methods Childhood Cancer Cost Study, a descriptive qualitative design was conducted. Parents discussed costs and their impact in a semistructured, audio-taped interview. A qualitative content analysis was used to analyze the transcribed data. RESULTS: Seventy-eight parents participated. Parents used several strategies to maintain financial stability. These strategies consisted of managing expenses, which entailed reducing living expenses and cutting unexpected cancer costs. Efforts to absorb these expenses required families to increase their debt while seeking ways to tap into available resources, including relying on their savings and leveraging their benefits and assets, increasing their paid work hours, relying on their support networks, and seeking help from philanthropy and government agencies for financial help. CONCLUSION: Parents used several strategies to manage the increased out-of-pocket expenses and reduced household income. IMPLICATIONS FOR PRACTICE: Our findings of the financial impact of cancer costs on families provide insight into needed practice and policy changes aimed at lessening the economic impact of a childhood cancer diagnosis on the family and allow healthcare professionals and researchers to pursue more in-depth cost assessments in the future.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".