Part I: A Quantitative Study of Social Risk Screening Acceptability in Patients and Caregivers
Bibliographic record
Abstract
INTRODUCTION: Despite recent growth in healthcare delivery-based social risk screening, little is known about patient perspectives on these activities. This study evaluates patient and caregiver acceptability of social risk screening. METHODS: This was a cross-sectional survey of 969 adult patients and adult caregivers of pediatric patients recruited from 6 primary care clinics and 4 emergency departments across 9 states. Survey items included the Center for Medicare and Medicaid Innovation Accountable Health Communities' social risk screening tool and questions about appropriateness of screening and comfort with including social risk data in electronic health records. Logistic regressions evaluated covariate associations with acceptability measures. Data collection occurred from July 2018 to February 2019; data analyses were conducted in February‒March 2019. RESULTS: Screening was reported as appropriate by 79% of participants; 65% reported comfort including social risks in electronic health records. In adjusted models, higher perceived screening appropriateness was associated with previous exposure to healthcare-based social risk screening (AOR=1.82, 95% CI=1.16, 2.88), trust in clinicians (AOR=1.55, 95% CI=1.00, 2.40), and recruitment from a primary care setting (AOR=1.70, 95% CI=1.23, 2.38). Lower appropriateness was associated with previous experience of healthcare discrimination (AOR=0.66, 95% CI=0.45, 0.95). Higher comfort with electronic health record documentation was associated with previously receiving assistance with social risks in a healthcare setting (AOR=1.47, 95% CI=1.04, 2.07). CONCLUSIONS: A strong majority of adult patients and caregivers of pediatric patients reported that social risk screening was appropriate. Most also felt comfortable including social risk data in electronic health records. Although multiple factors influenced acceptability, the effects were moderate to small. These findings suggest that lack of patient acceptability is unlikely to be a major implementation barrier. SUPPLEMENT INFORMATION: This article is part of a supplement entitled Identifying and Intervening on Social Needs in Clinical Settings: Evidence and Evidence Gaps, which is sponsored by the Agency for Healthcare Research and Quality of the U.S. Department of Health and Human Services, Kaiser Permanente, and the Robert Wood Johnson Foundation.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".