Clinician Experiences and Attitudes Regarding Screening for Social Determinants of Health in a Large Integrated Health System
Bibliographic record
Abstract
BACKGROUND: Clinical screening for basic social needs-such as food and housing insecurity-is becoming more common as health systems develop programs to address social determinants of health. Clinician attitudes toward such programs are largely unexplored. OBJECTIVE: To describe the attitudes and experiences of social needs screening among a variety of clinicians and other health care professionals. RESEARCH DESIGN: Multicenter electronic and paper-based survey. SUBJECTS: Two hundred fifty-eight clinicians including primarily physicians, social workers, nurses, and pharmacists from a large integrated health system in Southern California. MEASURES: Level of agreement with prompts exploring attitudes toward and barriers to screening and addressing social needs in different clinical settings. RESULTS: Overall, most health professionals supported social needs screening in clinical settings (84%). Only a minority (41%) of clinicians expressed confidence in their ability to address social needs, and less than a quarter (23%) routinely screen for social needs currently. Clinicians perceived lack of time to ask (60%) and resources (50%) to address social needs as their most significant barriers. We found differences by health profession in attitudes toward and barriers to screening for social needs, with physicians more likely to cite time constraints as a barrier. CONCLUSIONS: Clinicians largely support social needs programs, but they also recognize key barriers to their implementation. Health systems interested in implementing social needs programs should consider the clinician perspective around the time and resources required for such programs and address these perceived barriers.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".