101 Screening for Psychosocial Risk in Families of Children with Medical Complexity (CMC)
Bibliographic record
Abstract
Abstract Background Children with medical complexity (CMC) are defined by their medical fragility, dependence on assistive technology and substantial care needs. Family caregivers of CMC have unique challenges, such as prolonged hospitalizations and poor care coordination, which result in extensive caregiver stress. There is a great need to quantify the level of psychosocial stress and resilience in these caregivers to allow for appropriate allocation of health care resources. The Psychosocial Assessment Tool (PAT) is a brief parent-reported screening tool for measuring psychosocial risk in caregivers of pediatric populations. This is the first study to use the PAT in children with medical complexity. Objectives To quantify psychosocial risk in family caregivers of children diagnosed with medical complexity. To identify predictors of caregiver distress based on their sociodemographic factors. It was hypothesized that the extensive health demands of CMC will result in high amounts of chronic, ongoing caregiver distress relative to the previously studied pediatric populations. Design/Methods This cross-sectional study was conducted at The Hospital for Sick Children, Toronto, Canada. Family caregivers of children with medical complexity completed the PAT questionnaires during regularly scheduled Long-Term Ventilation and Complex Care clinic visits. Based on the overall PAT scores, caregivers were stratified as “Universal” low risk (<1.0), “Targeted” intermediate risk (1.0 to 1.9), or “Clinical” high risk (≥2.0). Multiple linear regression analysis was performed to examine the effect of sociodemographic variables and illness severity on total PAT scores. Comparisons with previous pediatric studies were made using T-test statistics. Results 136 [103 females (76%)] family caregivers completed the study. Mean PAT score was 1.17 (SD = 0.740). 61 (44.85%) caregivers were classified as Universal risk, 60 (44.12%) as Targeted risk, and 15 (11.03%) as Clinical risk. Compared to previously studied pediatric populations, our CMC have the second-highest overall PAT scores, which are also substantially weighted towards the higher risk categories (Table 1). Multiple linear regression analysis demonstrated that subjective report of financial hardship by caregivers is a significant predictor of total PAT scores (p < 0.05). Conclusion Family caregivers of children with medical complexity report PAT scores amongst the highest of all pediatric populations. These caregivers experience significant psychosocial distress, demonstrated by larger proportions of caregivers in the Targeted and Clinical risk categories. Therefore, psychosocial interventions including financial assistance are urgently needed in this 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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".