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Record W3089060166 · doi:10.1111/1475-6773.13416

The Role of Primary Care Practices in Screening for Patient Social Needs in the United States and Other High‐Income Countries

2020· article· en· W3089060166 on OpenAlexaboutno aff
Roosa Tikkanen, Arnav Shah, Eric C. Schneider

Bibliographic record

VenueHealth Services Research · 2020
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
Fundersnot available
KeywordsCommonwealthMedicinePovertyLonelinessSocial isolationNeeds assessmentPopulationFamily medicineEnvironmental healthEconomic growthPolitical scienceEconomics

Abstract

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Research Objective Unmet social needs including poverty, housing, or food instability run deeper in the United States compared with ten other high‐income countries, as identified by previous Commonwealth Fund International Patient Surveys. US primary care physicians (PCPs) are increasingly tasked with screening for these needs, given their centrality in providing coordinated and patient‐centered care. This study compares social needs screening rates among US PCPs with those in other high‐income countries, and explores factors associated with screening. Study Design Cross‐sectional analysis of data from the 2019 Commonwealth Fund Survey of Primary Care Physicians, which included a random sample of PCPs contacted between January and June 2019. Screening for social needs was defined as the share of PCPs that reported that they or other personnel in their practice usually screen patients for unmet needs relating to housing, financial insecurity, food insecurity, transportation, utilities, domestic violence, or social isolation/loneliness. We explored the likelihood of screening for any of these needs using logistic regressions adjusted for practice characteristics and demographic variables. Population Studied 13 184 PCPs in Australia, Canada, France, Germany, the Netherlands, New Zealand, Norway, Sweden, Switzerland, the United Kingdom, and the United States (500‐2569 per country). Principal Findings US PCPs were significantly more likely to report screening for social needs (29%) than in most other countries (9‐25%) except France (29%), as well as most individual needs including financial security (19% vs 4‐14% in all other countries), transportation needs (14% vs 2‐12% all other), and social isolation/loneliness (15% vs <1‐7% all other). US physicians remained significantly more likely than those in other countries to screen for social needs, even after adjusting for practice and demographic characteristics (ORs: 0.23‐0.71, P s < .001 vs other countries except France). PCPs in practices with a social worker were more likely to screen than those without (31% vs. 19%; OR 1.80, P < .001). Among US PCPs, those in practices employing community health workers (45% vs 26%; OR 2.11, P < .001), that saw predominantly (≥50%) Medicaid patients (39% vs 28%; OR 1.53, P = .002), and federally qualified health centers (43% vs 27%; OR 1.97, P < .001) were significantly more likely to screen. Across countries, PCPs who screened were slightly more likely to report job‐related stress (52%) than those who did not (48%; OR 1.12, P = .010, adjusted); however, this was lower for PCPs that had a social worker at their practice (42% vs 55%; OR 0.58, P < .001). Conclusions US PCPs more often screen for social needs than those in most other high‐income countries. US practices that employ social workers or community health workers, as well as safety‐net settings, more often screened for social needs. PCP screening for social needs is associated with higher job‐related stress levels, which was attenuated by having a social worker at the practice. Implications for Policy or Practice US primary care physicians, particularly those in safety‐net settings, may be more often tasked with screening for patient social needs than their counterparts abroad, in part because of a lack of a robust social safety‐net system. To avoid social screening potentially contributing to physician stress, health systems and payers may want to consider team‐based approaches to screening. Primary Funding Source This study was supported by the Commonwealth Fund.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.249
GPT teacher head0.525
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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