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Enregistrement W2901943128 · doi:10.1017/cts.2018.238

2250 Barriers to healthcare after the Affordable Care Act: A qualitative study of Los Angeles safety net patients’ experiences with insurance and healthcare

2018· article· en· W2901943128 sur OpenAlexaboutno aff
Sonali Saluja, Danny McCormick, Michael R. Cousineau, Janina L. Morrison, Michael E. Hochman

Notice bibliographique

RevueJournal of Clinical and Translational Science · 2018
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealthcare Policy and Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSafety netMedicaidPovertyHealth careQuarter (Canadian coin)Government (linguistics)PopulationHealth insuranceBusinessQualitative researchMedicineFamily medicineEnvironmental healthGeographyEconomic growthSociology

Résumé

récupéré en direct d'OpenAlex

OBJECTIVES/SPECIFIC AIMS: N/A. METHODS/STUDY POPULATION: Over a million people gained insurance in Los Angeles (LA) County under the Affordable Care Act (ACA). The vast majority gained Medicaid—government sponsored insurance with low-cost sharing. LA County also made significant investments in the safety net including a program called MyHealthLA, which provides primary and tertiary care for the residually uninsured including poor undocumented individuals at specific sites. Despite this insurance expansion, approximately 3 quarters of a million people in the county remain uninsured. Regardless of insurance status, nearly a quarter of LA County residents reported having difficulty obtaining needed medical care, and among those making less than the poverty level, 43% had difficulties. There is still much to understand about barriers to obtaining insurance and accessing healthcare in Los Angeles in the post-ACA era. Our primary objective was to understand how safety net patients are obtaining, maintaining and using their insurance after the ACA. Specifically we hope to understand the barriers and drivers of these three processes. RESULTS/ANTICIPATED RESULTS: We conducted a qualitative study of 34 safety net patients with 3 different insurance types in LA County. We conducted in-person interviews with adult patients (ages 18–64 years), who had either MediCal, MyHealthLA, or were unsinsured. Our interview guide was based on existing literature, a previous qualitative study conducted in Massachusetts and input from experts in the field. We pilot tested our interviews in English and Spanish and then recruited our participants from 3 sites: LAC+USC (a publically funded county hospital), The Wellness Center (a resource center for safety net patients), and White Memorial Medical Center (a private safety net hospital). We approached patients in the ED and urgent care waiting rooms and obtained informed consent for this IRB approved study. We excluded patients who were non-English and non-Spanish speaking or too ill to interview. We recorded interviews, which were then transcribed and translated into English by a contracted agency. We analyzed our interviews using a framework approach, which included a set of a priori codes from the literature as well as emerging codes from patient responses. We will check a sample of our transcripts for coding consistency (aiming for an inter-rater reliability of >80%). DISCUSSION/SIGNIFICANCE OF IMPACT: We recruited a diverse group of patients that were demographically representative of those who gained insurance under the ACA (childless adults making less than 138% of the Federal Poverty Level). Our preliminary results (based on 17 transcripts), suggest that patients, regardless of insurance type have difficulty accessing primary care. We identified seven domains under the broader theme of barriers to accessing primary care: finding a primary care clinic or physician (PCP), getting timely appointments, geography and transportation, continuity of care, using the Emergency Department (ED) or urgent care as a PCP, switching PCPs or clinics, and cost or coverage.

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)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,168
Score d'incertitude au seuil0,280

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,076
Tête enseignante GPT0,397
Écart entre enseignants0,321 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
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

Citations1
Publié2018
Routes d'admission1
Résumé présentoui

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