Screening for Social Determinants of Health in a Pediatric Respiratory Clinic
Bibliographic record
Abstract
OBJECTIVE: We aimed to evaluate the feasibility and early outcomes of a locally developed screening tool to identify social determinants of health (SDOH) in a pediatric respiratory clinic. METHODS: An interdisciplinary SDOH working group in the Division of Respiratory Medicine at the Hospital for Sick Children (SickKids) in Toronto formed consensus on a 10-question tool addressing SDOH that was embedded into the electronic health record system (Epic) in outpatient clinics. Health care providers were trained to address these questions with caregivers of patients attending clinics over 3 months (February to April 2023). A positive screen result on the SDOH questionnaire was actioned with a social work referral for further evaluation and resource navigation. RESULTS: Within the study period, 320 families were approached and 98% (n = 313) agreed to participate. Of these, 69/313 (22%) were referred to social work and 35/69 (50%) had an active intervention. CONCLUSION: There are high levels of unmet social needs in ambulatory clinics underscoring the importance of addressing SDOH and providing support to children and their caregivers.
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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 | 0.004 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".