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Enregistrement 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 sur OpenAlexaboutno aff
Roosa Tikkanen, Arnav Shah, Eric C. Schneider

Notice bibliographique

RevueHealth Services Research · 2020
Typearticle
Langueen
DomaineHealth Professions
ThématiqueFood Security and Health in Diverse Populations
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCommonwealthMedicinePovertyLonelinessSocial isolationNeeds assessmentPopulationFamily medicineEnvironmental healthEconomic growthPolitical scienceEconomics

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,201
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0030,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,249
Tête enseignante GPT0,525
Écart entre enseignants0,276 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2020
Routes d'admission1
Résumé présentoui

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