Screening for social determinants of health in clinical care: moving from the margins to the mainstream
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Screening for the social determinants of health in clinical practice is still widely debated. METHODS: A scoping review was used to (1) explore the various screening tools that are available to identify social risk, (2) examine the impact that screening for social determinants has on health and social outcomes, and (3) identify factors that promote the uptake of screening in routine clinical care. RESULTS: Over the last two decades, a growing number of screening tools have been developed to help frontline health workers ask about the social determinants of health in clinical care. In addition to clinical practice guidelines that recommend screening for specific areas of social risk (e.g., violence in pregnancy), there is also a growing body of evidence exploring the use of screening or case finding for identifying multiple domains of social risk (e.g., poverty, food insecurity, violence, unemployment, and housing problems). CONCLUSION: There is increasing traction within the medical field for improving social history taking and integrating more formal screening for social determinants of health within clinical practice. There is also a growing number of high-quality evidence-based reviews that identify interventions that are effective in promoting health equity at the individual patient level, and at broader community and structural levels.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it