In support of point-of-care social needs screening: The effects of five social determinants on the health of children with chronic diseases in British Columbia
Bibliographic record
Abstract
Prior to introducing social needs screening into our subspecialty clinics, we first wanted to understand the health effects of the major social challenges facing children with chronic diseases in British Columbia. Using a strict prospective methodology, avoiding use of health databases and proxy end points, we studied the effects of five social health determinants (distance from care, family income, gender, ethnicity, caregiver education), on health outcomes in three groups of children with chronic diseases: cystic fibrosis (CF), type 1 diabetes (T1D), chronic kidney disease (CKD). Social determinant data were collected at a face-to-face interview during a clinic visit. These were correlated with diagnosis-specific health outcomes, measured at the same visit. Main outcomes were: forced expired volume in 1 second (FEV1) (CF group), HbA1c (T1D group), estimated glomerular filtration rate (CKD group). We studied 270 children: 85 CF, 89 T1D and 96 CKD. In all three groups, children from families with annual income less than $45,000 had significantly worse health than those from families above this cut-off. Lower caregiver education was related to worse health in the CKD and T1D groups. We found no adverse health effects associated with distance from subspecialty care, patient ethnicity or gender. Even in a prosperous province, family poverty and lack of caregiver education still impose measurable adverse effects on the health of children with chronic diseases. We hope these results help support the integration of social needs screening into routine multidisciplinary outpatient clinics. Early detection of social problems and targeted interventions will hopefully help to equalize health outcomes between children from different social groups.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".