CARING FOR THE CAREGIVER: EXPLORING THE EXPERIENCE OF CAREGIVING FOR A CHID WITH MEDICAL COMPLEXITY
Bibliographic record
Abstract
Abstract BACKGROUND Children with medical complexity (CMC) represent a growing population in the paediatric healthcare system. CMCs’ multiple health needs, illness severity, and fragility lead to a high degree of caregiver burnout, parental employment loss, and other social and financial consequences. Healthcare providers must consider a holistic view of the family, including consideration of social determinants of health [SDOH]: socio-economic status, employment, quality of housing, availability of social support, and access to healthcare services. This study addresses a gap in current research by exploring how caring for a CMC exacerbates social inequalities, and how these challenges may be mitigated. OBJECTIVES 1. What are the SDOH impacting CMC and their families? 2. How do families report the impact of their child’s medical complexity on their social circumstances? DESIGN/METHODS A qualitative description approach was used to enable rich data collection through semi-structured interviews until thematic saturation was reached. Nine interviews were conducted with CMC caregivers who were chosen by purposeful sampling. The interview guide was developed by expert consultation and iteratively refined. Ethics approval and written consent were obtained. Interviews were recorded and transcribed verbatim. Three team members independently coded the interviews for recurrent themes to inform qualitative content analysis. RESULTS Caregiving for a CMC is all-consuming, requiring parents to take on roles including planner, medical professional, medical educator, and advocate. Parents of CMC report three major areas negatively impacted by caregiving: 1) physical and mental health; 2) personal relationships; and 3) finances. Additionally, three themes emerged describing enablers for resiliency: 1) the CMC’s health status when well or stable in hospital; 2) acceptance of one’s limitations as a caregiver and of the family’s ‘new normal’; and 3) broad supports including medical, personal, financial, and educational. CONCLUSION The wellbeing of CMC families is impacted by medical and social factors. An interdisciplinary model of care may offset some of the caregiver’s advocacy and medical educator responsibilities. Paediatricians can support families of CMC by providing comprehensive care for all components of the child’s health status, and by including routine assessment of the family’s SDOH. Finally, explicit discussion about parental expectations and caregiver burnout helps foster a positive therapeutic relationship with the family.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".