Building the Infrastructure to Integrate Social Care in a Safety Net Health System
Bibliographic record
Abstract
A recent National Academies report recommended that health systems invest in new infrastructure to integrate social and medical care. Although many health systems routinely screen patients for social concerns, few health systems achieve the recommended model of integration. In this critical case study in an urban safety net health system, we describe the human capital, operational redesign, and financial investment needed to implement the National Academy recommendations. Using data from this case study, we estimate that other health systems seeking to build and maintain this infrastructure would need to invest $1 million to $3 million per year. While health systems with robust existing resources may be able to bootstrap short-term funding to initiate this work, we conclude that long-term investments by insurers and other payers will be necessary for most health systems to achieve the recommended integration of medical and social care. Researchers seeking to test whether integrating social and medical care leads to better patient and population outcomes require access to health systems and communities who have already invested in this model infrastructure. ( Am J Public Health. 2024;114(6):619–625. https://doi.org/10.2105/AJPH.2024.307602 )
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".