Examining the Spillover Economic Impacts of Caregiving Among Families of Children With Medical Complexity to Inform Inclusive Economic Models: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Children with medical complexity (CMC) represent a heterogeneous group of children with multiple, chronic healthcare conditions. Caregivers of CMC experience a high intensity of caregiving that is often variable, extends across several networks of care, and often lasts for the entirety of the child's life. The economic impacts of caregiving are yet understudied in the CMC context. There have been recognized limitations to the sole use of quantitative methods when developing economic models of disease because they lack direct caregiver voice and context of caregiving activities and existing methods have been noted to be ableist. OBJECTIVE: The purpose of this study was to explore the economic spillover impacts of caregiving among families of CMC using their own words and perspectives with the intent of expanding caregiver-centered perspectives when developing economic models. METHODS: This study was a secondary analysis of a qualitative study that was conducted to examine family management practices among caregivers of CMC and their social networks. Caregivers of CMC were recruited through a Pediatric Complex Care clinic at an academic medical center in the mid-Atlantic region, USA. This study used inductive qualitative descriptive methods and the use of a template to define features of the person impacted and to define the economic construct as either a direct or indirect/spillover cost. RESULTS: Twenty caregivers were included in this study. Perspectives from the caregivers of CMC revealed several key themes: (1) time investment in caregiving - impacting the primary caregivers; (2) physical and mental health impacts - impacting the child themselves, siblings, and the primary caregivers; (3) impacts to leisure activities and self-care - impacting the child themselves, siblings, and the primary caregivers; (4) impacts to the social network/social capital. CONCLUSIONS: The themes described can be operationalized into inclusive family-centered models that represent the impacts of caregiving in the context of the family units of CMC. The use of qualitative methods to expand our development of quantitative economic models can be adapted to other populations where caregivers are involved in care. Caregivers can and should have an active voice in preference-based assessments that are operationalized in economic contexts to make them more inclusive. CLINICALTRIAL: n/a. INTERNATIONAL REGISTERED REPORT: RR2-10.2196/14810.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".