Out-of-pocket expenses reported by families of children with medical complexity
Bibliographic record
Abstract
Objectives: Due to their medical and technology dependence, families of children with medical complexity (CMC) have significant costs associated with care. Financial impact on families in general have been described, but detailed exploration of expenses in specific categories has not been systematically explored. Our objective was to describe out-of-pocket (OOP) expenses incurred by caregivers of CMC and to determine factors associated with increased expenditures. Methods: This is a secondary observational analysis of data primary caregiver-reported OOP expenses as part of a randomized control trial conducted in Ontario, Canada. Caregivers completed questionnaires reporting OOP costs. Descriptive statistics were utilized to report OOP expenses and a linear regression model was conducted. Results: 107 primary caregivers of CMC were included. The median (IQR) age of participants was 34.5 years (30.5 to 40.5) and 83.2% identified as the mother. The majority were married or common-law (86.9%) and 50.5% were employed. The participant's children [median (IQR) age 4.5 (2.2 to 9.7); 57.9% male] most commonly had a neurological/neuromuscular primary diagnosis (46.1%) and 88% utilized medical technology. Total OOP expenses were $8,639 CDN annually (IQR = $4,661 to $31,326) with substantial expenses related to childcare/homemaking, travel to appointments, hospitalizations, and device costs. No factors associated with greater likelihood of OOP expenses were identified. A P-value of <0.05 was considered significant. Conclusion: Caregivers of CMC incur significant OOP expenses related to the care of their children resulting in financial burden. Future exploration of the financial impact on caregiver productivity, employment, and identification of resources to mitigate OOP expenses will be important for this patient population.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".