Assessment and Documentation of Social Determinants of Health Among Health Care Providers: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Research clearly demonstrates social determinants of health (SDOH) impact health outcomes. Provider consideration of patient SDOH in prevention and treatment planning is critical for improved health care quality and health equity. Despite awareness of the connections between SDOH and improved population health, research demonstrates few providers document patient SDOH. OBJECTIVE: This qualitative study aimed to better understand the barriers and facilitators of SDOH assessment, documentation, and referral in different health care settings and roles. METHODS: Individual semistructured interviews were conducted with practicing health care providers in South Carolina between August 25, 2022, and September 2, 2022. Participants were recruited via community partners' web-based newsletters or listservs using a purposive sampling design. An interview guide with 19 questions was used to explore the following research question: How do SDOH impact patient health and what are the facilitators and barriers experienced by multidisciplinary health care providers assessing and documenting patient SDOH? RESULTS: Participants (N=5) included a neonatal intensive care unit registered nurse, a nurse practitioner, a certified nurse midwife, a family and preventive medicine physician, and a counselor (licensed clinical social worker) with careers spanning 12 to 32 years. Participant responses are presented according to the following 5 themes: participants' understanding of SDOH for the patient population, assessment and documentation practices, referrals to other providers and community-based resources, barriers and facilitators of SDOH assessment and documentation, and SDOH assessment and documentation training preferences. Overall, participants were aware of the importance of including patient SDOH in assessment and intervention but noted a variety of institutional and interpersonal barriers to assessment and documentation, including time constraints, perceptions of stigma around discussion of SDOH, and limited referral protocols. CONCLUSIONS: Incentivizing inclusion of patient SDOH in health care must be facilitated from the top down, so assessment and documentation can be universally implemented in a pragmatic way that works for providers in a variety of roles and settings for the betterment of health care quality, health equity, and improved population health outcomes. Partnering with community organizations can serve to augment health care organizations' resource and referral availability for addressing patients' social needs.
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How this classification was reachedexpand
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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".