Clinician and Staff Perspectives on a Social Drivers of Health Program Implementation
Bibliographic record
Abstract
INTRODUCTION: Health systems are increasingly pursuing efforts to screen for and address social drivers of health (SDOH), the nonmedical factors that contribute to health outcomes and inequities. A large integrated health system (Intermountain Health) launched a program in 2019 to universally screen for and address SDOH. METHODS: Five primary care clinics within Intermountain were purposefully chosen for diversity of setting and practice type (family medicine and pediatric). We conducted 20 semistructured interviews with frontline clinicians and staff from 7/1/2020 to 9/1/2020 to explore attitudes related to feasibility, workflow processes, and facilitators and barriers to successful implementation. We conducted an inductive-deductive analysis to identify key themes and best practices. RESULTS: Five clinics conducted 16,659 SDOH patient screenings from 12/1/2019 to 11/30/2020 (705 to 7,723 screens per clinic with rates ranging from 7.4% to 52.8% per clinic). Respondent perspectives about the program were mixed. Dominant implementation barriers included staff time constraints, limited availability of social services, and reduced morale. Key facilitators included triage protocols for positive screens independent of the primary care clinician, standardizing previsit digital screening, and instilling a culture of shared ownership through education and team SDOH-focused huddles. CONCLUSIONS: This evaluation of an early systemwide SDOH program implementation called into question the feasibility of universal screening in primary care given staff time constraints and social service availability. Future investigations should explore the impact of targeted screening approaches in diverse clinical settings and quantifying trade offs between SDOH programs and other clinical and organizational priorities.
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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 | 0.040 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".