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Record 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 on OpenAlexaboutno aff
Sonali Saluja, Danny McCormick, Michael R. Cousineau, Janina L. Morrison, Michael E. Hochman

Bibliographic record

VenueJournal of Clinical and Translational Science · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsSafety netMedicaidPovertyHealth careQuarter (Canadian coin)Government (linguistics)PopulationHealth insuranceBusinessQualitative researchMedicineFamily medicineEnvironmental healthGeographyEconomic growthSociology

Abstract

fetched live from 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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.076
GPT teacher head0.397
Teacher spread0.321 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2018
Admission routes1
Has abstractyes

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